Overview

Dataset statistics

Number of variables48
Number of observations3983
Missing cells58139
Missing cells (%)30.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.5 MiB
Average record size in memory384.0 B

Variable types

Categorical33
Unsupported3
Numeric12

Alerts

pickup_method has constant value "来店" Constant
pickup_shop_code has constant value "638" Constant
pickup_shop_name has constant value "新千歳空港店(ニコレンお客様大賞受賞店)" Constant
return_shop_code has constant value "638" Constant
return_shop_name has constant value "新千歳空港店(ニコレンお客様大賞受賞店)" Constant
transmission has constant value "AT" Constant
drop_off_fee has constant value "0" Constant
night_fee_pickup has constant value "0" Constant
night_fee_return has constant value "0" Constant
non_taxable_amount has constant value "0" Constant
mobile_career has constant value "i" Constant
cancellation_classification has constant value "ノーチャージキャンセル" Constant
rakuten_booking_number has a high cardinality: 3983 distinct values High cardinality
request_date_time has a high cardinality: 3965 distinct values High cardinality
request_confirmed_date_time has a high cardinality: 3116 distinct values High cardinality
cancel_request_date_time has a high cardinality: 748 distinct values High cardinality
cancellation_day_time has a high cardinality: 499 distinct values High cardinality
flight_number has a high cardinality: 708 distinct values High cardinality
accommodation_name has a high cardinality: 64 distinct values High cardinality
accommodation_address has a high cardinality: 64 distinct values High cardinality
accommodation_phone_number has a high cardinality: 64 distinct values High cardinality
accommodation_booking_number has a high cardinality: 81 distinct values High cardinality
pickup_date_time has a high cardinality: 3237 distinct values High cardinality
return_date_time has a high cardinality: 3153 distinct values High cardinality
memo has a high cardinality: 86 distinct values High cardinality
booking_status is highly correlated with total_amountHigh correlation
basic_price is highly correlated with subtotal_amount and 2 other fieldsHigh correlation
subtotal_amount is highly correlated with basic_price and 2 other fieldsHigh correlation
total_amount is highly correlated with booking_status and 3 other fieldsHigh correlation
taxable_amount is highly correlated with basic_price and 2 other fieldsHigh correlation
basic_price is highly correlated with subtotal_amount and 2 other fieldsHigh correlation
subtotal_amount is highly correlated with basic_price and 2 other fieldsHigh correlation
total_amount is highly correlated with basic_price and 2 other fieldsHigh correlation
taxable_amount is highly correlated with basic_price and 2 other fieldsHigh correlation
booking_status is highly correlated with total_amountHigh correlation
basic_price is highly correlated with subtotal_amount and 2 other fieldsHigh correlation
subtotal_amount is highly correlated with basic_price and 2 other fieldsHigh correlation
total_amount is highly correlated with booking_status and 3 other fieldsHigh correlation
taxable_amount is highly correlated with basic_price and 2 other fieldsHigh correlation
booking_status is highly correlated with cancellation_reasonHigh correlation
namber_of_passengers is highly correlated with accommodation_name and 4 other fieldsHigh correlation
number_of_children is highly correlated with accommodation_booking_number and 1 other fieldsHigh correlation
accommodation_name is highly correlated with namber_of_passengers and 12 other fieldsHigh correlation
accommodation_address is highly correlated with namber_of_passengers and 12 other fieldsHigh correlation
accommodation_phone_number is highly correlated with namber_of_passengers and 12 other fieldsHigh correlation
accommodation_booking_number is highly correlated with namber_of_passengers and 15 other fieldsHigh correlation
company_car_class_code is highly correlated with accommodation_name and 7 other fieldsHigh correlation
detail_car_class_code is highly correlated with accommodation_name and 8 other fieldsHigh correlation
detail_car_class_name is highly correlated with accommodation_name and 8 other fieldsHigh correlation
campaign is highly correlated with company_car_class_code and 4 other fieldsHigh correlation
car_attribute is highly correlated with detail_car_class_code and 2 other fieldsHigh correlation
basic_price is highly correlated with accommodation_name and 8 other fieldsHigh correlation
options_total_fee is highly correlated with accommodation_name and 8 other fieldsHigh correlation
subtotal_amount is highly correlated with accommodation_booking_number and 6 other fieldsHigh correlation
coupon is highly correlated with accommodation_name and 7 other fieldsHigh correlation
point is highly correlated with accommodation_name and 4 other fieldsHigh correlation
total_amount is highly correlated with basic_price and 4 other fieldsHigh correlation
taxable_amount is highly correlated with accommodation_booking_number and 6 other fieldsHigh correlation
memo is highly correlated with namber_of_passengers and 16 other fieldsHigh correlation
cancellation_reason is highly correlated with booking_status and 6 other fieldsHigh correlation
payment_method is highly correlated with accommodation_name and 4 other fieldsHigh correlation
company_booking_number has 3983 (100.0%) missing values Missing
rakupack_booking_number has 3981 (99.9%) missing values Missing
request_confirmed_date_time has 272 (6.8%) missing values Missing
cancel_request_date_time has 3227 (81.0%) missing values Missing
cancellation_day_time has 3470 (87.1%) missing values Missing
accommodation_name has 3900 (97.9%) missing values Missing
accommodation_address has 3900 (97.9%) missing values Missing
accommodation_phone_number has 3900 (97.9%) missing values Missing
accommodation_booking_number has 3900 (97.9%) missing values Missing
campaign has 138 (3.5%) missing values Missing
car_attribute has 3777 (94.8%) missing values Missing
memo has 3894 (97.8%) missing values Missing
cancellation_reason has 3921 (98.4%) missing values Missing
mobile_career has 3981 (99.9%) missing values Missing
mobile_model has 3981 (99.9%) missing values Missing
cancellation_classification has 3929 (98.6%) missing values Missing
answer has 3983 (100.0%) missing values Missing
rakuten_booking_number is uniformly distributed Uniform
rakupack_booking_number is uniformly distributed Uniform
request_date_time is uniformly distributed Uniform
request_confirmed_date_time is uniformly distributed Uniform
cancel_request_date_time is uniformly distributed Uniform
cancellation_day_time is uniformly distributed Uniform
accommodation_name is uniformly distributed Uniform
accommodation_address is uniformly distributed Uniform
accommodation_phone_number is uniformly distributed Uniform
accommodation_booking_number is uniformly distributed Uniform
pickup_date_time is uniformly distributed Uniform
return_date_time is uniformly distributed Uniform
memo is uniformly distributed Uniform
mobile_model is uniformly distributed Uniform
rakuten_booking_number has unique values Unique
company_booking_number is an unsupported type, check if it needs cleaning or further analysis Unsupported
options is an unsupported type, check if it needs cleaning or further analysis Unsupported
answer is an unsupported type, check if it needs cleaning or further analysis Unsupported
company_car_class_code has 2159 (54.2%) zeros Zeros
detail_car_class_code has 134 (3.4%) zeros Zeros
detail_car_class_name has 134 (3.4%) zeros Zeros
options_total_fee has 1005 (25.2%) zeros Zeros
coupon has 3481 (87.4%) zeros Zeros
point has 3500 (87.9%) zeros Zeros
total_amount has 790 (19.8%) zeros Zeros
cancel_fee has 3906 (98.1%) zeros Zeros

Reproduction

Analysis started2022-09-12 19:16:20.400491
Analysis finished2022-09-12 19:17:01.619633
Duration41.22 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

booking_status
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size31.2 KiB
1
3211 
0
772 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3983
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
13211
80.6%
0772
 
19.4%

Length

2022-09-12T22:17:01.725446image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-12T22:17:01.869374image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
13211
80.6%
0772
 
19.4%

Most occurring characters

ValueCountFrequency (%)
13211
80.6%
0772
 
19.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3983
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
13211
80.6%
0772
 
19.4%

Most occurring scripts

ValueCountFrequency (%)
Common3983
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
13211
80.6%
0772
 
19.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII3983
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
13211
80.6%
0772
 
19.4%

rakuten_booking_number
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct3983
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size31.2 KiB
RC32457434637117616
 
1
RC52458192376558736
 
1
RC22458189615417853
 
1
RC22458189712198434
 
1
RC22458189784879004
 
1
Other values (3978)
3978 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters75677
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3983 ?
Unique (%)100.0%

Sample

1st rowRC32457434637117616
2nd rowRC32457434645237633
3rd rowRC62457437572294437
4th rowRC72457438509836329
5th rowRC72457438821737647

Common Values

ValueCountFrequency (%)
RC324574346371176161
 
< 0.1%
RC524581923765587361
 
< 0.1%
RC224581896154178531
 
< 0.1%
RC224581897121984341
 
< 0.1%
RC224581897848790041
 
< 0.1%
RC324581904752813131
 
< 0.1%
RC324581905888619921
 
< 0.1%
RC324581906495224421
 
< 0.1%
RC324581906697524921
 
< 0.1%
RC424581915476656381
 
< 0.1%
Other values (3973)3973
99.7%

Length

2022-09-12T22:17:01.978748image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
rc324574346371176161
 
< 0.1%
rc624574446525014441
 
< 0.1%
rc424574495024832551
 
< 0.1%
rc624574375722944371
 
< 0.1%
rc724574385098363291
 
< 0.1%
rc724574388217376471
 
< 0.1%
rc124574390062078541
 
< 0.1%
rc224574403910409021
 
< 0.1%
rc224574405898016681
 
< 0.1%
rc224574408492128531
 
< 0.1%
Other values (3973)3973
99.7%

Most occurring characters

ValueCountFrequency (%)
49780
12.9%
59540
12.6%
29201
12.2%
77279
9.6%
86727
8.9%
35661
7.5%
65492
7.3%
14819
6.4%
04734
6.3%
94478
5.9%
Other values (2)7966
10.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number67711
89.5%
Uppercase Letter7966
 
10.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
49780
14.4%
59540
14.1%
29201
13.6%
77279
10.8%
86727
9.9%
35661
8.4%
65492
8.1%
14819
7.1%
04734
7.0%
94478
6.6%
Uppercase Letter
ValueCountFrequency (%)
R3983
50.0%
C3983
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common67711
89.5%
Latin7966
 
10.5%

Most frequent character per script

Common
ValueCountFrequency (%)
49780
14.4%
59540
14.1%
29201
13.6%
77279
10.8%
86727
9.9%
35661
8.4%
65492
8.1%
14819
7.1%
04734
7.0%
94478
6.6%
Latin
ValueCountFrequency (%)
R3983
50.0%
C3983
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII75677
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
49780
12.9%
59540
12.6%
29201
12.2%
77279
9.6%
86727
8.9%
35661
7.5%
65492
7.3%
14819
6.4%
04734
6.3%
94478
5.9%
Other values (2)7966
10.5%

company_booking_number
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing3983
Missing (%)100.0%
Memory size31.2 KiB

rakupack_booking_number
Categorical

MISSING
UNIFORM

Distinct2
Distinct (%)100.0%
Missing3981
Missing (%)99.9%
Memory size31.2 KiB
RA12457614083242529
RA62457675026323329

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters38
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st rowRA12457614083242529
2nd rowRA62457675026323329

Common Values

ValueCountFrequency (%)
RA124576140832425291
 
< 0.1%
RA624576750263233291
 
< 0.1%
(Missing)3981
99.9%

Length

2022-09-12T22:17:02.101021image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-12T22:17:02.239939image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
ra124576140832425291
50.0%
ra624576750263233291
50.0%

Most occurring characters

ValueCountFrequency (%)
28
21.1%
44
10.5%
54
10.5%
64
10.5%
34
10.5%
73
 
7.9%
R2
 
5.3%
A2
 
5.3%
12
 
5.3%
02
 
5.3%
Other values (2)3
 
7.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number34
89.5%
Uppercase Letter4
 
10.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
28
23.5%
44
11.8%
54
11.8%
64
11.8%
34
11.8%
73
 
8.8%
12
 
5.9%
02
 
5.9%
92
 
5.9%
81
 
2.9%
Uppercase Letter
ValueCountFrequency (%)
R2
50.0%
A2
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common34
89.5%
Latin4
 
10.5%

Most frequent character per script

Common
ValueCountFrequency (%)
28
23.5%
44
11.8%
54
11.8%
64
11.8%
34
11.8%
73
 
8.8%
12
 
5.9%
02
 
5.9%
92
 
5.9%
81
 
2.9%
Latin
ValueCountFrequency (%)
R2
50.0%
A2
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII38
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
28
21.1%
44
10.5%
54
10.5%
64
10.5%
34
10.5%
73
 
7.9%
R2
 
5.3%
A2
 
5.3%
12
 
5.3%
02
 
5.3%
Other values (2)3
 
7.9%

request_date_time
Categorical

HIGH CARDINALITY
UNIFORM

Distinct3965
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Memory size31.2 KiB
2017/9/13 1:27
 
2
2017/10/16 0:12
 
2
2017/6/21 11:08
 
2
2016/5/31 22:09
 
2
2017/9/3 18:26
 
2
Other values (3960)
3973 

Length

Max length16
Median length15
Mean length14.72181773
Min length13

Characters and Unicode

Total characters58637
Distinct characters13
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3947 ?
Unique (%)99.1%

Sample

1st row2016/2/16 17:41
2nd row2016/2/16 17:55
3rd row2016/2/19 15:53
4th row2016/2/20 14:09
5th row2016/2/20 22:49

Common Values

ValueCountFrequency (%)
2017/9/13 1:272
 
0.1%
2017/10/16 0:122
 
0.1%
2017/6/21 11:082
 
0.1%
2016/5/31 22:092
 
0.1%
2017/9/3 18:262
 
0.1%
2017/4/21 15:032
 
0.1%
2018/5/14 23:172
 
0.1%
2018/6/22 22:462
 
0.1%
2018/8/19 13:292
 
0.1%
2018/7/15 15:122
 
0.1%
Other values (3955)3963
99.5%

Length

2022-09-12T22:17:02.367394image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2018/7/2424
 
0.3%
2018/7/2320
 
0.3%
2017/9/1318
 
0.2%
2017/5/1717
 
0.2%
2017/4/2717
 
0.2%
2017/9/216
 
0.2%
2018/7/2516
 
0.2%
2018/7/2716
 
0.2%
2016/6/1415
 
0.2%
2018/10/415
 
0.2%
Other values (2183)7792
97.8%

Most occurring characters

ValueCountFrequency (%)
110799
18.4%
28741
14.9%
/7966
13.6%
06459
11.0%
3983
 
6.8%
:3983
 
6.8%
83182
 
5.4%
72969
 
5.1%
62666
 
4.5%
32416
 
4.1%
Other values (3)5473
9.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number42705
72.8%
Other Punctuation11949
 
20.4%
Space Separator3983
 
6.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
110799
25.3%
28741
20.5%
06459
15.1%
83182
 
7.5%
72969
 
7.0%
62666
 
6.2%
32416
 
5.7%
52079
 
4.9%
41884
 
4.4%
91510
 
3.5%
Other Punctuation
ValueCountFrequency (%)
/7966
66.7%
:3983
33.3%
Space Separator
ValueCountFrequency (%)
3983
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common58637
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
110799
18.4%
28741
14.9%
/7966
13.6%
06459
11.0%
3983
 
6.8%
:3983
 
6.8%
83182
 
5.4%
72969
 
5.1%
62666
 
4.5%
32416
 
4.1%
Other values (3)5473
9.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII58637
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
110799
18.4%
28741
14.9%
/7966
13.6%
06459
11.0%
3983
 
6.8%
:3983
 
6.8%
83182
 
5.4%
72969
 
5.1%
62666
 
4.5%
32416
 
4.1%
Other values (3)5473
9.3%

request_confirmed_date_time
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct3116
Distinct (%)84.0%
Missing272
Missing (%)6.8%
Memory size31.2 KiB
2017/7/13 13:45
 
7
2016/7/21 12:11
 
7
2017/8/29 16:50
 
7
2016/6/26 23:50
 
6
2017/9/5 9:19
 
6
Other values (3111)
3678 

Length

Max length16
Median length15
Mean length14.67286446
Min length13

Characters and Unicode

Total characters54451
Distinct characters13
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2706 ?
Unique (%)72.9%

Sample

1st row2016/2/16 18:18
2nd row2016/2/19 16:01
3rd row2016/2/20 15:43
4th row2016/2/21 8:58
5th row2016/2/21 10:41

Common Values

ValueCountFrequency (%)
2017/7/13 13:457
 
0.2%
2016/7/21 12:117
 
0.2%
2017/8/29 16:507
 
0.2%
2016/6/26 23:506
 
0.2%
2017/9/5 9:196
 
0.2%
2016/10/14 8:126
 
0.2%
2017/9/9 14:196
 
0.2%
2018/11/2 19:506
 
0.2%
2017/7/18 8:466
 
0.2%
2017/9/9 14:186
 
0.2%
Other values (3106)3648
91.6%
(Missing)272
 
6.8%

Length

2022-09-12T22:17:02.495589image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2016/6/2638
 
0.5%
2016/6/2531
 
0.4%
2017/5/2024
 
0.3%
2018/7/2423
 
0.3%
2018/11/223
 
0.3%
2018/7/2822
 
0.3%
2016/6/2421
 
0.3%
2018/8/1619
 
0.3%
2017/9/919
 
0.3%
2017/6/1119
 
0.3%
Other values (1795)7183
96.8%

Most occurring characters

ValueCountFrequency (%)
110402
19.1%
/7422
13.6%
27298
13.4%
05886
10.8%
3711
 
6.8%
:3711
 
6.8%
83182
 
5.8%
72943
 
5.4%
62472
 
4.5%
52030
 
3.7%
Other values (3)5394
9.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number39607
72.7%
Other Punctuation11133
 
20.4%
Space Separator3711
 
6.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
110402
26.3%
27298
18.4%
05886
14.9%
83182
 
8.0%
72943
 
7.4%
62472
 
6.2%
52030
 
5.1%
31997
 
5.0%
41736
 
4.4%
91661
 
4.2%
Other Punctuation
ValueCountFrequency (%)
/7422
66.7%
:3711
33.3%
Space Separator
ValueCountFrequency (%)
3711
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common54451
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
110402
19.1%
/7422
13.6%
27298
13.4%
05886
10.8%
3711
 
6.8%
:3711
 
6.8%
83182
 
5.8%
72943
 
5.4%
62472
 
4.5%
52030
 
3.7%
Other values (3)5394
9.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII54451
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
110402
19.1%
/7422
13.6%
27298
13.4%
05886
10.8%
3711
 
6.8%
:3711
 
6.8%
83182
 
5.8%
72943
 
5.4%
62472
 
4.5%
52030
 
3.7%
Other values (3)5394
9.9%

cancel_request_date_time
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct748
Distinct (%)98.9%
Missing3227
Missing (%)81.0%
Memory size31.2 KiB
2017/7/20 17:40
 
2
2016/6/14 21:38
 
2
2017/8/19 19:08
 
2
2017/3/28 10:23
 
2
2017/4/27 23:14
 
2
Other values (743)
746 

Length

Max length16
Median length15
Mean length14.69179894
Min length13

Characters and Unicode

Total characters11107
Distinct characters13
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique740 ?
Unique (%)97.9%

Sample

1st row2016/2/16 17:47
2nd row2016/3/5 20:19
3rd row2016/5/30 13:30
4th row2016/2/27 1:17
5th row2016/2/27 20:25

Common Values

ValueCountFrequency (%)
2017/7/20 17:402
 
0.1%
2016/6/14 21:382
 
0.1%
2017/8/19 19:082
 
0.1%
2017/3/28 10:232
 
0.1%
2017/4/27 23:142
 
0.1%
2018/10/2 11:472
 
0.1%
2016/2/27 20:252
 
0.1%
2018/8/7 10:172
 
0.1%
2018/4/18 0:131
 
< 0.1%
2018/3/3 22:001
 
< 0.1%
Other values (738)738
 
18.5%
(Missing)3227
81.0%

Length

2022-09-12T22:17:02.691682image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2016/6/147
 
0.5%
2017/9/46
 
0.4%
2018/9/66
 
0.4%
2017/9/25
 
0.3%
2018/8/85
 
0.3%
2018/8/75
 
0.3%
12:105
 
0.3%
10:354
 
0.3%
2017/9/144
 
0.3%
2016/7/184
 
0.3%
Other values (1036)1461
96.6%

Most occurring characters

ValueCountFrequency (%)
12033
18.3%
21655
14.9%
/1512
13.6%
01260
11.3%
756
 
6.8%
:756
 
6.8%
8587
 
5.3%
7583
 
5.2%
6467
 
4.2%
3433
 
3.9%
Other values (3)1065
9.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number8083
72.8%
Other Punctuation2268
 
20.4%
Space Separator756
 
6.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
12033
25.2%
21655
20.5%
01260
15.6%
8587
 
7.3%
7583
 
7.2%
6467
 
5.8%
3433
 
5.4%
5383
 
4.7%
4360
 
4.5%
9322
 
4.0%
Other Punctuation
ValueCountFrequency (%)
/1512
66.7%
:756
33.3%
Space Separator
ValueCountFrequency (%)
756
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common11107
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
12033
18.3%
21655
14.9%
/1512
13.6%
01260
11.3%
756
 
6.8%
:756
 
6.8%
8587
 
5.3%
7583
 
5.2%
6467
 
4.2%
3433
 
3.9%
Other values (3)1065
9.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII11107
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
12033
18.3%
21655
14.9%
/1512
13.6%
01260
11.3%
756
 
6.8%
:756
 
6.8%
8587
 
5.3%
7583
 
5.2%
6467
 
4.2%
3433
 
3.9%
Other values (3)1065
9.6%

cancellation_day_time
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct499
Distinct (%)97.3%
Missing3470
Missing (%)87.1%
Memory size31.2 KiB
2017/5/15 7:21
 
3
2016/8/11 21:25
 
3
2017/8/20 9:42
 
2
2018/9/8 9:37
 
2
2018/10/24 15:03
 
2
Other values (494)
501 

Length

Max length16
Median length15
Mean length14.68421053
Min length13

Characters and Unicode

Total characters7533
Distinct characters13
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique487 ?
Unique (%)94.9%

Sample

1st row2016/3/5 20:44
2nd row2016/5/30 13:30
3rd row2016/2/28 7:52
4th row2016/3/4 9:56
5th row2016/5/20 14:51

Common Values

ValueCountFrequency (%)
2017/5/15 7:213
 
0.1%
2016/8/11 21:253
 
0.1%
2017/8/20 9:422
 
0.1%
2018/9/8 9:372
 
0.1%
2018/10/24 15:032
 
0.1%
2018/4/5 14:062
 
0.1%
2016/7/13 14:322
 
0.1%
2018/9/6 15:422
 
0.1%
2018/9/6 15:412
 
0.1%
2017/7/20 17:402
 
0.1%
Other values (489)491
 
12.3%
(Missing)3470
87.1%

Length

2022-09-12T22:17:02.896554image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2018/9/66
 
0.6%
2017/5/155
 
0.5%
2016/8/115
 
0.5%
2017/8/125
 
0.5%
2018/7/84
 
0.4%
2018/6/64
 
0.4%
2018/12/54
 
0.4%
12:104
 
0.4%
10:484
 
0.4%
2017/8/54
 
0.4%
Other values (723)981
95.6%

Most occurring characters

ValueCountFrequency (%)
11488
19.8%
/1026
13.6%
21000
13.3%
0836
11.1%
513
 
6.8%
:513
 
6.8%
8436
 
5.8%
7421
 
5.6%
6322
 
4.3%
5262
 
3.5%
Other values (3)716
9.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number5481
72.8%
Other Punctuation1539
 
20.4%
Space Separator513
 
6.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
11488
27.1%
21000
18.2%
0836
15.3%
8436
 
8.0%
7421
 
7.7%
6322
 
5.9%
5262
 
4.8%
3244
 
4.5%
4241
 
4.4%
9231
 
4.2%
Other Punctuation
ValueCountFrequency (%)
/1026
66.7%
:513
33.3%
Space Separator
ValueCountFrequency (%)
513
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common7533
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
11488
19.8%
/1026
13.6%
21000
13.3%
0836
11.1%
513
 
6.8%
:513
 
6.8%
8436
 
5.8%
7421
 
5.6%
6322
 
4.3%
5262
 
3.5%
Other values (3)716
9.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII7533
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
11488
19.8%
/1026
13.6%
21000
13.3%
0836
11.1%
513
 
6.8%
:513
 
6.8%
8436
 
5.8%
7421
 
5.6%
6322
 
4.3%
5262
 
3.5%
Other values (3)716
9.5%

namber_of_passengers
Real number (ℝ≥0)

HIGH CORRELATION

Distinct8
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.225458197
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.2 KiB
2022-09-12T22:17:03.023308image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile5
Maximum8
Range7
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.275817121
Coefficient of variation (CV)0.5732828964
Kurtosis2.401192195
Mean2.225458197
Median Absolute Deviation (MAD)1
Skewness1.400636124
Sum8864
Variance1.627709327
MonotonicityNot monotonic
2022-09-12T22:17:03.142546image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
21514
38.0%
11290
32.4%
3540
 
13.6%
4415
 
10.4%
5137
 
3.4%
647
 
1.2%
721
 
0.5%
819
 
0.5%
ValueCountFrequency (%)
11290
32.4%
21514
38.0%
3540
 
13.6%
4415
 
10.4%
5137
 
3.4%
647
 
1.2%
721
 
0.5%
819
 
0.5%
ValueCountFrequency (%)
819
 
0.5%
721
 
0.5%
647
 
1.2%
5137
 
3.4%
4415
 
10.4%
3540
 
13.6%
21514
38.0%
11290
32.4%

number_of_children
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size31.2 KiB
0
3689 
1
 
204
2
 
78
3
 
10
4
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3983
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
03689
92.6%
1204
 
5.1%
278
 
2.0%
310
 
0.3%
42
 
0.1%

Length

2022-09-12T22:17:03.303447image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-12T22:17:03.472341image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
03689
92.6%
1204
 
5.1%
278
 
2.0%
310
 
0.3%
42
 
0.1%

Most occurring characters

ValueCountFrequency (%)
03689
92.6%
1204
 
5.1%
278
 
2.0%
310
 
0.3%
42
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3983
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03689
92.6%
1204
 
5.1%
278
 
2.0%
310
 
0.3%
42
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common3983
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
03689
92.6%
1204
 
5.1%
278
 
2.0%
310
 
0.3%
42
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII3983
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03689
92.6%
1204
 
5.1%
278
 
2.0%
310
 
0.3%
42
 
0.1%

flight_number
Categorical

HIGH CARDINALITY

Distinct708
Distinct (%)17.8%
Missing2
Missing (%)0.1%
Memory size31.2 KiB
航空便利用なし
396 
MM103
 
83
GK153
 
72
GK105
 
70
SKY171
 
64
Other values (703)
3296 

Length

Max length7
Median length6
Mean length5.825420749
Min length1

Characters and Unicode

Total characters23191
Distinct characters66
Distinct categories4 ?
Distinct scripts4 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique382 ?
Unique (%)9.6%

Sample

1st rowSKY771
2nd rowSKY771
3rd rowANA4837
4th rowBC171
5th rowANA4723

Common Values

ValueCountFrequency (%)
航空便利用なし396
 
9.9%
MM10383
 
2.1%
GK15372
 
1.8%
GK10570
 
1.8%
SKY17164
 
1.6%
SKY70362
 
1.6%
SKY76157
 
1.4%
GK18147
 
1.2%
JAL50346
 
1.2%
GK10343
 
1.1%
Other values (698)3041
76.3%

Length

2022-09-12T22:17:04.055982image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
航空便利用なし396
 
9.9%
mm10384
 
2.1%
sky17180
 
2.0%
sky76177
 
1.9%
sky70376
 
1.9%
gk10575
 
1.9%
gk15374
 
1.9%
gk18149
 
1.2%
jal50346
 
1.2%
gk10345
 
1.1%
Other values (559)2979
74.8%

Most occurring characters

ValueCountFrequency (%)
12875
 
12.4%
A2507
 
10.8%
01750
 
7.5%
51514
 
6.5%
71454
 
6.3%
31079
 
4.7%
N968
 
4.2%
K905
 
3.9%
J887
 
3.8%
9690
 
3.0%
Other values (56)8562
36.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10924
47.1%
Uppercase Letter8542
36.8%
Other Letter2772
 
12.0%
Lowercase Letter953
 
4.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A2507
29.3%
N968
 
11.3%
K905
 
10.6%
J887
 
10.4%
L553
 
6.5%
G493
 
5.8%
M449
 
5.3%
S435
 
5.1%
Y413
 
4.8%
D259
 
3.0%
Other values (16)673
 
7.9%
Lowercase Letter
ValueCountFrequency (%)
a186
19.5%
k145
15.2%
s121
12.7%
y119
12.5%
n73
 
7.7%
j66
 
6.9%
l40
 
4.2%
m28
 
2.9%
e22
 
2.3%
o21
 
2.2%
Other values (13)132
13.9%
Decimal Number
ValueCountFrequency (%)
12875
26.3%
01750
16.0%
51514
13.9%
71454
13.3%
31079
 
9.9%
9690
 
6.3%
2568
 
5.2%
8393
 
3.6%
6366
 
3.4%
4235
 
2.2%
Other Letter
ValueCountFrequency (%)
396
14.3%
396
14.3%
396
14.3%
396
14.3%
396
14.3%
396
14.3%
便396
14.3%

Most occurring scripts

ValueCountFrequency (%)
Common10924
47.1%
Latin9495
40.9%
Han1980
 
8.5%
Hiragana792
 
3.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
A2507
26.4%
N968
 
10.2%
K905
 
9.5%
J887
 
9.3%
L553
 
5.8%
G493
 
5.2%
M449
 
4.7%
S435
 
4.6%
Y413
 
4.3%
D259
 
2.7%
Other values (39)1626
17.1%
Common
ValueCountFrequency (%)
12875
26.3%
01750
16.0%
51514
13.9%
71454
13.3%
31079
 
9.9%
9690
 
6.3%
2568
 
5.2%
8393
 
3.6%
6366
 
3.4%
4235
 
2.2%
Han
ValueCountFrequency (%)
396
20.0%
396
20.0%
396
20.0%
396
20.0%
便396
20.0%
Hiragana
ValueCountFrequency (%)
396
50.0%
396
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII20419
88.0%
CJK1980
 
8.5%
Hiragana792
 
3.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
12875
14.1%
A2507
12.3%
01750
 
8.6%
51514
 
7.4%
71454
 
7.1%
31079
 
5.3%
N968
 
4.7%
K905
 
4.4%
J887
 
4.3%
9690
 
3.4%
Other values (49)5790
28.4%
CJK
ValueCountFrequency (%)
396
20.0%
396
20.0%
396
20.0%
396
20.0%
便396
20.0%
Hiragana
ValueCountFrequency (%)
396
50.0%
396
50.0%

accommodation_name
Categorical

HIGH CARDINALITY
HIGH CORRELATION
MISSING
UNIFORM

Distinct64
Distinct (%)77.1%
Missing3900
Missing (%)97.9%
Memory size31.2 KiB
千歳ステーションホテル(旧:ハイパーホテル千歳)
 
4
札幌第一ホテル
 
3
JRイン旭川
 
3
ホテル函館ロイヤル
 
2
丸駒温泉旅館
 
2
Other values (59)
69 

Length

Max length37
Median length22
Mean length13.28915663
Min length6

Characters and Unicode

Total characters1103
Distinct characters215
Distinct categories11 ?
Distinct scripts5 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique49 ?
Unique (%)59.0%

Sample

1st rowアパホテル<札幌>
2nd rowホテルリブマックス千歳
3rd rowオスパーコート宮前
4th rowドーミーインPREMIUM札幌
5th row札幌第一ホテル

Common Values

ValueCountFrequency (%)
千歳ステーションホテル(旧:ハイパーホテル千歳)4
 
0.1%
札幌第一ホテル3
 
0.1%
JRイン旭川3
 
0.1%
ホテル函館ロイヤル2
 
0.1%
丸駒温泉旅館2
 
0.1%
ながぬま温泉2
 
0.1%
ホテルリリーフ札幌すすきの2
 
0.1%
星野リゾート トマム ザ・タワー2
 
0.1%
登別温泉 登別グランドホテル2
 
0.1%
ホテルエミシア札幌2
 
0.1%
Other values (54)59
 
1.5%
(Missing)3900
97.9%

Length

2022-09-12T22:17:04.221881image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ホテル5
 
4.4%
千歳ステーションホテル(旧:ハイパーホテル千歳)4
 
3.5%
jrイン旭川3
 
2.7%
札幌第一ホテル3
 
2.7%
登別グランドホテル2
 
1.8%
洞爺湖温泉2
 
1.8%
ホテルウィングインターナショナル苫小牧2
 
1.8%
サッポロメッツ2
 
1.8%
スーパーホテル札幌・すすきの2
 
1.8%
ホテルクラビーサッポロ2
 
1.8%
Other values (76)86
76.1%

Most occurring characters

ValueCountFrequency (%)
80
 
7.3%
77
 
7.0%
69
 
6.3%
53
 
4.8%
33
 
3.0%
 30
 
2.7%
28
 
2.5%
28
 
2.5%
25
 
2.3%
20
 
1.8%
Other values (205)660
59.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter922
83.6%
Modifier Letter53
 
4.8%
Uppercase Letter44
 
4.0%
Space Separator30
 
2.7%
Other Punctuation15
 
1.4%
Close Punctuation11
 
1.0%
Open Punctuation11
 
1.0%
Lowercase Letter8
 
0.7%
Math Symbol4
 
0.4%
Decimal Number3
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
80
 
8.7%
77
 
8.4%
69
 
7.5%
33
 
3.6%
28
 
3.0%
28
 
3.0%
25
 
2.7%
20
 
2.2%
20
 
2.2%
19
 
2.1%
Other values (165)523
56.7%
Uppercase Letter
ValueCountFrequency (%)
5
 
11.4%
4
 
9.1%
4
 
9.1%
4
 
9.1%
3
 
6.8%
B2
 
4.5%
2
 
4.5%
2
 
4.5%
2
 
4.5%
2
 
4.5%
Other values (10)14
31.8%
Lowercase Letter
ValueCountFrequency (%)
2
25.0%
2
25.0%
2
25.0%
1
12.5%
1
12.5%
Other Punctuation
ValueCountFrequency (%)
7
46.7%
6
40.0%
2
 
13.3%
Decimal Number
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%
Close Punctuation
ValueCountFrequency (%)
10
90.9%
1
 
9.1%
Open Punctuation
ValueCountFrequency (%)
10
90.9%
1
 
9.1%
Math Symbol
ValueCountFrequency (%)
2
50.0%
2
50.0%
Modifier Letter
ValueCountFrequency (%)
53
100.0%
Space Separator
ValueCountFrequency (%)
 30
100.0%
Dash Punctuation
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Katakana603
54.7%
Han273
24.8%
Common129
 
11.7%
Latin52
 
4.7%
Hiragana46
 
4.2%

Most frequent character per script

Han
ValueCountFrequency (%)
28
 
10.3%
28
 
10.3%
14
 
5.1%
14
 
5.1%
13
 
4.8%
13
 
4.8%
8
 
2.9%
6
 
2.2%
5
 
1.8%
5
 
1.8%
Other values (83)139
50.9%
Katakana
ValueCountFrequency (%)
80
 
13.3%
77
 
12.8%
69
 
11.4%
33
 
5.5%
25
 
4.1%
20
 
3.3%
20
 
3.3%
19
 
3.2%
13
 
2.2%
13
 
2.2%
Other values (53)234
38.8%
Latin
ValueCountFrequency (%)
5
 
9.6%
4
 
7.7%
4
 
7.7%
4
 
7.7%
3
 
5.8%
B2
 
3.8%
2
 
3.8%
2
 
3.8%
2
 
3.8%
2
 
3.8%
Other values (15)22
42.3%
Hiragana
ValueCountFrequency (%)
10
21.7%
10
21.7%
5
10.9%
2
 
4.3%
2
 
4.3%
2
 
4.3%
2
 
4.3%
2
 
4.3%
1
 
2.2%
1
 
2.2%
Other values (9)9
19.6%
Common
ValueCountFrequency (%)
53
41.1%
 30
23.3%
10
 
7.8%
10
 
7.8%
7
 
5.4%
6
 
4.7%
2
 
1.6%
2
 
1.6%
2
 
1.6%
2
 
1.6%
Other values (5)5
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
Katakana662
60.0%
CJK273
24.8%
None119
 
10.8%
Hiragana46
 
4.2%
ASCII3
 
0.3%

Most frequent character per block

Katakana
ValueCountFrequency (%)
80
 
12.1%
77
 
11.6%
69
 
10.4%
53
 
8.0%
33
 
5.0%
25
 
3.8%
20
 
3.0%
20
 
3.0%
19
 
2.9%
13
 
2.0%
Other values (55)253
38.2%
None
ValueCountFrequency (%)
 30
25.2%
10
 
8.4%
10
 
8.4%
7
 
5.9%
5
 
4.2%
4
 
3.4%
4
 
3.4%
4
 
3.4%
3
 
2.5%
2
 
1.7%
Other values (26)40
33.6%
CJK
ValueCountFrequency (%)
28
 
10.3%
28
 
10.3%
14
 
5.1%
14
 
5.1%
13
 
4.8%
13
 
4.8%
8
 
2.9%
6
 
2.2%
5
 
1.8%
5
 
1.8%
Other values (83)139
50.9%
Hiragana
ValueCountFrequency (%)
10
21.7%
10
21.7%
5
10.9%
2
 
4.3%
2
 
4.3%
2
 
4.3%
2
 
4.3%
2
 
4.3%
1
 
2.2%
1
 
2.2%
Other values (9)9
19.6%
ASCII
ValueCountFrequency (%)
B2
66.7%
H1
33.3%

accommodation_address
Categorical

HIGH CARDINALITY
HIGH CORRELATION
MISSING
UNIFORM

Distinct64
Distinct (%)77.1%
Missing3900
Missing (%)97.9%
Memory size31.2 KiB
北海道千歳市千代田町7-1789-3
 
4
北海道札幌市中央区南7条西1丁目12-7
 
3
北海道旭川市宮下通7丁目2番5号
 
3
北海道函館市大森町16-9
 
2
北海道千歳市幌美内7**支笏湖奥、唯一の秘湯【丸駒温泉】
 
2
Other values (59)
69 

Length

Max length54
Median length25
Mean length17.93975904
Min length12

Characters and Unicode

Total characters1489
Distinct characters196
Distinct categories8 ?
Distinct scripts4 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique49 ?
Unique (%)59.0%

Sample

1st row北海道札幌市中央区南二条西七丁目10-1
2nd row北海道千歳市清水町3-14-1
3rd row北海道旭川市宮下通16丁目4155-16
4th row北海道札幌市中央区南2条西6丁目4-1
5th row北海道札幌市中央区南7条西1丁目12-7

Common Values

ValueCountFrequency (%)
北海道千歳市千代田町7-1789-34
 
0.1%
北海道札幌市中央区南7条西1丁目12-73
 
0.1%
北海道旭川市宮下通7丁目2番5号3
 
0.1%
北海道函館市大森町16-92
 
0.1%
北海道千歳市幌美内7**支笏湖奥、唯一の秘湯【丸駒温泉】2
 
0.1%
北海道夕張郡長沼町東6線北42
 
0.1%
北海道札幌市中央区南8条西3-1-42
 
0.1%
北海道勇払郡占冠村字中トマム2
 
0.1%
北海道登別市登別温泉町1542
 
0.1%
北海道札幌市厚別区厚別中央2条5丁目(新札幌駅前)2
 
0.1%
Other values (54)59
 
1.5%
(Missing)3900
97.9%

Length

2022-09-12T22:17:04.372098image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
北海道千歳市千代田町7-1789-34
 
4.5%
北海道旭川市宮下通7丁目2番5号3
 
3.4%
北海道札幌市中央区南7条西1丁目12-73
 
3.4%
北海道札幌市厚別区厚別中央2条5丁目(新札幌駅前)2
 
2.3%
北海道苫小牧市表町5-7-12
 
2.3%
北海道千歳市千代田町6-12
 
2.3%
北海道札幌市中央区南6条西2丁目8-12
 
2.3%
サッポロファクトリー西館2
 
2.3%
北海道札幌市中央区北2条東3丁目2
 
2.3%
北海道札幌市北区北17条西5丁目2-502
 
2.3%
Other values (58)64
72.7%

Most occurring characters

ValueCountFrequency (%)
104
 
7.0%
83
 
5.6%
83
 
5.6%
172
 
4.8%
-68
 
4.6%
64
 
4.3%
41
 
2.8%
235
 
2.4%
35
 
2.4%
35
 
2.4%
Other values (186)869
58.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter1102
74.0%
Decimal Number288
 
19.3%
Dash Punctuation71
 
4.8%
Open Punctuation7
 
0.5%
Close Punctuation7
 
0.5%
Other Punctuation6
 
0.4%
Space Separator5
 
0.3%
Modifier Letter3
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
104
 
9.4%
83
 
7.5%
83
 
7.5%
64
 
5.8%
41
 
3.7%
35
 
3.2%
35
 
3.2%
35
 
3.2%
33
 
3.0%
32
 
2.9%
Other values (157)557
50.5%
Decimal Number
ValueCountFrequency (%)
172
25.0%
235
12.2%
332
11.1%
528
 
9.7%
727
 
9.4%
421
 
7.3%
620
 
6.9%
814
 
4.9%
912
 
4.2%
09
 
3.1%
Other values (8)18
 
6.2%
Dash Punctuation
ValueCountFrequency (%)
-68
95.8%
3
 
4.2%
Open Punctuation
ValueCountFrequency (%)
4
57.1%
3
42.9%
Close Punctuation
ValueCountFrequency (%)
4
57.1%
3
42.9%
Other Punctuation
ValueCountFrequency (%)
*4
66.7%
2
33.3%
Space Separator
ValueCountFrequency (%)
3
60.0%
 2
40.0%
Modifier Letter
ValueCountFrequency (%)
3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Han1038
69.7%
Common387
 
26.0%
Katakana47
 
3.2%
Hiragana17
 
1.1%

Most frequent character per script

Han
ValueCountFrequency (%)
104
 
10.0%
83
 
8.0%
83
 
8.0%
64
 
6.2%
41
 
3.9%
35
 
3.4%
35
 
3.4%
35
 
3.4%
33
 
3.2%
32
 
3.1%
Other values (129)493
47.5%
Common
ValueCountFrequency (%)
172
18.6%
-68
17.6%
235
9.0%
332
8.3%
528
 
7.2%
727
 
7.0%
421
 
5.4%
620
 
5.2%
814
 
3.6%
912
 
3.1%
Other values (19)58
15.0%
Katakana
ValueCountFrequency (%)
7
14.9%
6
12.8%
6
12.8%
4
 
8.5%
3
 
6.4%
3
 
6.4%
2
 
4.3%
2
 
4.3%
2
 
4.3%
2
 
4.3%
Other values (6)10
21.3%
Hiragana
ValueCountFrequency (%)
4
23.5%
3
17.6%
1
 
5.9%
1
 
5.9%
1
 
5.9%
1
 
5.9%
1
 
5.9%
1
 
5.9%
1
 
5.9%
1
 
5.9%
Other values (2)2
11.8%

Most occurring blocks

ValueCountFrequency (%)
CJK1038
69.7%
ASCII345
 
23.2%
Katakana50
 
3.4%
None39
 
2.6%
Hiragana17
 
1.1%

Most frequent character per block

CJK
ValueCountFrequency (%)
104
 
10.0%
83
 
8.0%
83
 
8.0%
64
 
6.2%
41
 
3.9%
35
 
3.4%
35
 
3.4%
35
 
3.4%
33
 
3.2%
32
 
3.1%
Other values (129)493
47.5%
ASCII
ValueCountFrequency (%)
172
20.9%
-68
19.7%
235
10.1%
332
9.3%
528
 
8.1%
727
 
7.8%
421
 
6.1%
620
 
5.8%
814
 
4.1%
912
 
3.5%
Other values (3)16
 
4.6%
Katakana
ValueCountFrequency (%)
7
14.0%
6
12.0%
6
12.0%
4
 
8.0%
3
 
6.0%
3
 
6.0%
3
 
6.0%
2
 
4.0%
2
 
4.0%
2
 
4.0%
Other values (7)12
24.0%
None
ValueCountFrequency (%)
7
17.9%
4
10.3%
4
10.3%
3
7.7%
3
7.7%
3
7.7%
3
7.7%
2
 
5.1%
2
 
5.1%
2
 
5.1%
Other values (5)6
15.4%
Hiragana
ValueCountFrequency (%)
4
23.5%
3
17.6%
1
 
5.9%
1
 
5.9%
1
 
5.9%
1
 
5.9%
1
 
5.9%
1
 
5.9%
1
 
5.9%
1
 
5.9%
Other values (2)2
11.8%

accommodation_phone_number
Categorical

HIGH CARDINALITY
HIGH CORRELATION
MISSING
UNIFORM

Distinct64
Distinct (%)77.1%
Missing3900
Missing (%)97.9%
Memory size31.2 KiB
0123-49-3000
 
4
011-530-1102
 
3
0166-24-8888
 
3
0138-26-8181
 
2
0123-25-2341
 
2
Other values (59)
69 

Length

Max length12
Median length12
Mean length11.98795181
Min length11

Characters and Unicode

Total characters995
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique49 ?
Unique (%)59.0%

Sample

1st row011-281-8111
2nd row0123-23-8100
3rd row0166-25-3200
4th row011-232-0011
5th row011-530-1102

Common Values

ValueCountFrequency (%)
0123-49-30004
 
0.1%
011-530-11023
 
0.1%
0166-24-88883
 
0.1%
0138-26-81812
 
0.1%
0123-25-23412
 
0.1%
0123-88-24082
 
0.1%
011-520-65502
 
0.1%
0167-58-11112
 
0.1%
0143-84-21012
 
0.1%
011-895-88112
 
0.1%
Other values (54)59
 
1.5%
(Missing)3900
97.9%

Length

2022-09-12T22:17:04.508424image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0123-49-30004
 
4.8%
0166-24-88883
 
3.6%
011-530-11023
 
3.6%
011-895-88112
 
2.4%
0144-33-03332
 
2.4%
011-726-55112
 
2.4%
011-521-90002
 
2.4%
011-242-11112
 
2.4%
0123-26-11552
 
2.4%
0143-84-21012
 
2.4%
Other values (54)59
71.1%

Most occurring characters

ValueCountFrequency (%)
1236
23.7%
-165
16.6%
0151
15.2%
2102
10.3%
380
 
8.0%
559
 
5.9%
454
 
5.4%
653
 
5.3%
853
 
5.3%
728
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number830
83.4%
Dash Punctuation165
 
16.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1236
28.4%
0151
18.2%
2102
12.3%
380
 
9.6%
559
 
7.1%
454
 
6.5%
653
 
6.4%
853
 
6.4%
728
 
3.4%
914
 
1.7%
Dash Punctuation
ValueCountFrequency (%)
-165
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common995
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1236
23.7%
-165
16.6%
0151
15.2%
2102
10.3%
380
 
8.0%
559
 
5.9%
454
 
5.4%
653
 
5.3%
853
 
5.3%
728
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII995
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1236
23.7%
-165
16.6%
0151
15.2%
2102
10.3%
380
 
8.0%
559
 
5.9%
454
 
5.4%
653
 
5.3%
853
 
5.3%
728
 
2.8%

accommodation_booking_number
Categorical

HIGH CARDINALITY
HIGH CORRELATION
MISSING
UNIFORM

Distinct81
Distinct (%)97.6%
Missing3900
Missing (%)97.9%
Memory size31.2 KiB
RYa06z8vz3
 
2
RYa07upkmx
 
2
RYa085s9i6
 
1
RYa085ap4u
 
1
RYa084h2jf
 
1
Other values (76)
76 

Length

Max length12
Median length10
Mean length10.24096386
Min length10

Characters and Unicode

Total characters850
Distinct characters35
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique79 ?
Unique (%)95.2%

Sample

1st rowRYa05nwdf6
2nd rowRYa05iwk9x
3rd rowRYa05nz60e
4th rowRYa05r8ykw
5th rowRYa05rbpap

Common Values

ValueCountFrequency (%)
RYa06z8vz32
 
0.1%
RYa07upkmx2
 
0.1%
RYa085s9i61
 
< 0.1%
RYa085ap4u1
 
< 0.1%
RYa084h2jf1
 
< 0.1%
RYa082i8iy1
 
< 0.1%
RYa080pyzz1
 
< 0.1%
RYa07w4x8x1
 
< 0.1%
RYa07w259b1
 
< 0.1%
RYa07snr131
 
< 0.1%
Other values (71)71
 
1.8%
(Missing)3900
97.9%

Length

2022-09-12T22:17:04.672322image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
rya06z8vz32
 
2.4%
rya07upkmx2
 
2.4%
rya070m66f1
 
1.2%
rya06ephes1
 
1.2%
rya05r8ykw1
 
1.2%
rya05rbpap1
 
1.2%
rya05s7fj11
 
1.2%
rya05r07451
 
1.2%
rya060jts61
 
1.2%
rya061t56w1
 
1.2%
Other values (71)71
85.5%

Most occurring characters

ValueCountFrequency (%)
a100
 
11.8%
092
 
10.8%
R83
 
9.8%
Y83
 
9.8%
745
 
5.3%
842
 
4.9%
634
 
4.0%
527
 
3.2%
920
 
2.4%
118
 
2.1%
Other values (25)306
36.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter358
42.1%
Decimal Number316
37.2%
Uppercase Letter166
19.5%
Connector Punctuation10
 
1.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a100
27.9%
x17
 
4.7%
f17
 
4.7%
m16
 
4.5%
k15
 
4.2%
z15
 
4.2%
p14
 
3.9%
i13
 
3.6%
y13
 
3.6%
d13
 
3.6%
Other values (12)125
34.9%
Decimal Number
ValueCountFrequency (%)
092
29.1%
745
14.2%
842
13.3%
634
 
10.8%
527
 
8.5%
920
 
6.3%
118
 
5.7%
215
 
4.7%
413
 
4.1%
310
 
3.2%
Uppercase Letter
ValueCountFrequency (%)
R83
50.0%
Y83
50.0%
Connector Punctuation
ValueCountFrequency (%)
_10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin524
61.6%
Common326
38.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
a100
19.1%
R83
15.8%
Y83
15.8%
x17
 
3.2%
f17
 
3.2%
m16
 
3.1%
k15
 
2.9%
z15
 
2.9%
p14
 
2.7%
i13
 
2.5%
Other values (14)151
28.8%
Common
ValueCountFrequency (%)
092
28.2%
745
13.8%
842
12.9%
634
 
10.4%
527
 
8.3%
920
 
6.1%
118
 
5.5%
215
 
4.6%
413
 
4.0%
_10
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII850
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a100
 
11.8%
092
 
10.8%
R83
 
9.8%
Y83
 
9.8%
745
 
5.3%
842
 
4.9%
634
 
4.0%
527
 
3.2%
920
 
2.4%
118
 
2.1%
Other values (25)306
36.0%

pickup_method
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size31.2 KiB
来店
3983 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters7966
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row来店
2nd row来店
3rd row来店
4th row来店
5th row来店

Common Values

ValueCountFrequency (%)
来店3983
100.0%

Length

2022-09-12T22:17:04.817318image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-12T22:17:04.955547image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
来店3983
100.0%

Most occurring characters

ValueCountFrequency (%)
3983
50.0%
3983
50.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter7966
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
3983
50.0%
3983
50.0%

Most occurring scripts

ValueCountFrequency (%)
Han7966
100.0%

Most frequent character per script

Han
ValueCountFrequency (%)
3983
50.0%
3983
50.0%

Most occurring blocks

ValueCountFrequency (%)
CJK7966
100.0%

Most frequent character per block

CJK
ValueCountFrequency (%)
3983
50.0%
3983
50.0%

pickup_date_time
Categorical

HIGH CARDINALITY
UNIFORM

Distinct3237
Distinct (%)81.3%
Missing0
Missing (%)0.0%
Memory size31.2 KiB
2018/8/14 10:00
 
10
2016/7/8 10:00
 
6
2018/7/23 10:00
 
6
2017/8/13 10:00
 
6
2017/6/6 11:00
 
6
Other values (3232)
3949 

Length

Max length16
Median length15
Mean length14.75470751
Min length13

Characters and Unicode

Total characters58768
Distinct characters13
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2690 ?
Unique (%)67.5%

Sample

1st row2016/2/21 14:00
2nd row2016/2/21 14:00
3rd row2016/2/29 19:30
4th row2016/3/12 10:00
5th row2016/2/21 15:00

Common Values

ValueCountFrequency (%)
2018/8/14 10:0010
 
0.3%
2016/7/8 10:006
 
0.2%
2018/7/23 10:006
 
0.2%
2017/8/13 10:006
 
0.2%
2017/6/6 11:006
 
0.2%
2017/6/16 10:006
 
0.2%
2018/8/11 10:005
 
0.1%
2016/10/7 10:005
 
0.1%
2018/4/21 12:305
 
0.1%
2016/7/1 10:005
 
0.1%
Other values (3227)3923
98.5%

Length

2022-09-12T22:17:05.061861image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
10:00798
 
10.0%
11:00310
 
3.9%
9:00289
 
3.6%
13:00215
 
2.7%
10:30215
 
2.7%
12:00203
 
2.5%
14:00189
 
2.4%
8:00161
 
2.0%
9:30150
 
1.9%
11:30129
 
1.6%
Other values (986)5307
66.6%

Most occurring characters

ValueCountFrequency (%)
012546
21.3%
110793
18.4%
/7966
13.6%
26564
11.2%
3983
 
6.8%
:3983
 
6.8%
82974
 
5.1%
72498
 
4.3%
32277
 
3.9%
62070
 
3.5%
Other values (3)3114
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number42836
72.9%
Other Punctuation11949
 
20.3%
Space Separator3983
 
6.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
012546
29.3%
110793
25.2%
26564
15.3%
82974
 
6.9%
72498
 
5.8%
32277
 
5.3%
62070
 
4.8%
91419
 
3.3%
4912
 
2.1%
5783
 
1.8%
Other Punctuation
ValueCountFrequency (%)
/7966
66.7%
:3983
33.3%
Space Separator
ValueCountFrequency (%)
3983
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common58768
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
012546
21.3%
110793
18.4%
/7966
13.6%
26564
11.2%
3983
 
6.8%
:3983
 
6.8%
82974
 
5.1%
72498
 
4.3%
32277
 
3.9%
62070
 
3.5%
Other values (3)3114
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII58768
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
012546
21.3%
110793
18.4%
/7966
13.6%
26564
11.2%
3983
 
6.8%
:3983
 
6.8%
82974
 
5.1%
72498
 
4.3%
32277
 
3.9%
62070
 
3.5%
Other values (3)3114
 
5.3%

pickup_shop_code
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size31.2 KiB
638
3983 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters11949
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row638
2nd row638
3rd row638
4th row638
5th row638

Common Values

ValueCountFrequency (%)
6383983
100.0%

Length

2022-09-12T22:17:05.192691image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-12T22:17:05.356808image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
6383983
100.0%

Most occurring characters

ValueCountFrequency (%)
63983
33.3%
33983
33.3%
83983
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number11949
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
63983
33.3%
33983
33.3%
83983
33.3%

Most occurring scripts

ValueCountFrequency (%)
Common11949
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
63983
33.3%
33983
33.3%
83983
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII11949
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
63983
33.3%
33983
33.3%
83983
33.3%

pickup_shop_name
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size31.2 KiB
新千歳空港店(ニコレンお客様大賞受賞店)
3983 

Length

Max length20
Median length20
Mean length20
Min length20

Characters and Unicode

Total characters79660
Distinct characters18
Distinct categories3 ?
Distinct scripts4 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row新千歳空港店(ニコレンお客様大賞受賞店)
2nd row新千歳空港店(ニコレンお客様大賞受賞店)
3rd row新千歳空港店(ニコレンお客様大賞受賞店)
4th row新千歳空港店(ニコレンお客様大賞受賞店)
5th row新千歳空港店(ニコレンお客様大賞受賞店)

Common Values

ValueCountFrequency (%)
新千歳空港店(ニコレンお客様大賞受賞店)3983
100.0%

Length

2022-09-12T22:17:05.476660image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-12T22:17:05.646572image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
新千歳空港店(ニコレンお客様大賞受賞店)3983
100.0%

Most occurring characters

ValueCountFrequency (%)
7966
 
10.0%
7966
 
10.0%
3983
 
5.0%
3983
 
5.0%
3983
 
5.0%
3983
 
5.0%
3983
 
5.0%
3983
 
5.0%
3983
 
5.0%
3983
 
5.0%
Other values (8)31864
40.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter71694
90.0%
Open Punctuation3983
 
5.0%
Close Punctuation3983
 
5.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
7966
 
11.1%
7966
 
11.1%
3983
 
5.6%
3983
 
5.6%
3983
 
5.6%
3983
 
5.6%
3983
 
5.6%
3983
 
5.6%
3983
 
5.6%
3983
 
5.6%
Other values (6)23898
33.3%
Open Punctuation
ValueCountFrequency (%)
3983
100.0%
Close Punctuation
ValueCountFrequency (%)
3983
100.0%

Most occurring scripts

ValueCountFrequency (%)
Han51779
65.0%
Katakana15932
 
20.0%
Common7966
 
10.0%
Hiragana3983
 
5.0%

Most frequent character per script

Han
ValueCountFrequency (%)
7966
15.4%
7966
15.4%
3983
7.7%
3983
7.7%
3983
7.7%
3983
7.7%
3983
7.7%
3983
7.7%
3983
7.7%
3983
7.7%
Katakana
ValueCountFrequency (%)
3983
25.0%
3983
25.0%
3983
25.0%
3983
25.0%
Common
ValueCountFrequency (%)
3983
50.0%
3983
50.0%
Hiragana
ValueCountFrequency (%)
3983
100.0%

Most occurring blocks

ValueCountFrequency (%)
CJK51779
65.0%
Katakana15932
 
20.0%
None7966
 
10.0%
Hiragana3983
 
5.0%

Most frequent character per block

CJK
ValueCountFrequency (%)
7966
15.4%
7966
15.4%
3983
7.7%
3983
7.7%
3983
7.7%
3983
7.7%
3983
7.7%
3983
7.7%
3983
7.7%
3983
7.7%
Katakana
ValueCountFrequency (%)
3983
25.0%
3983
25.0%
3983
25.0%
3983
25.0%
Hiragana
ValueCountFrequency (%)
3983
100.0%
None
ValueCountFrequency (%)
3983
50.0%
3983
50.0%

return_date_time
Categorical

HIGH CARDINALITY
UNIFORM

Distinct3153
Distinct (%)79.2%
Missing0
Missing (%)0.0%
Memory size31.2 KiB
2017/8/15 17:00
 
9
2018/8/16 17:00
 
8
2017/8/18 17:00
 
8
2018/8/15 17:00
 
8
2017/6/25 17:00
 
7
Other values (3148)
3943 

Length

Max length16
Median length15
Mean length14.89605825
Min length13

Characters and Unicode

Total characters59331
Distinct characters13
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2583 ?
Unique (%)64.9%

Sample

1st row2016/2/24 14:00
2nd row2016/2/24 14:00
3rd row2016/3/2 13:00
4th row2016/3/18 18:30
5th row2016/2/23 15:00

Common Values

ValueCountFrequency (%)
2017/8/15 17:009
 
0.2%
2018/8/16 17:008
 
0.2%
2017/8/18 17:008
 
0.2%
2018/8/15 17:008
 
0.2%
2017/6/25 17:007
 
0.2%
2018/7/23 17:007
 
0.2%
2018/1/7 19:007
 
0.2%
2017/6/26 17:006
 
0.2%
2017/6/8 16:006
 
0.2%
2018/8/13 17:006
 
0.2%
Other values (3143)3911
98.2%

Length

2022-09-12T22:17:05.770496image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
17:00850
 
10.7%
18:00376
 
4.7%
19:00370
 
4.6%
20:00362
 
4.5%
16:00262
 
3.3%
15:00217
 
2.7%
14:00154
 
1.9%
12:00142
 
1.8%
13:00136
 
1.7%
18:30122
 
1.5%
Other values (984)4975
62.5%

Most occurring characters

ValueCountFrequency (%)
012399
20.9%
110702
18.0%
/7966
13.4%
26710
11.3%
3983
 
6.7%
:3983
 
6.7%
73309
 
5.6%
83194
 
5.4%
62222
 
3.7%
31658
 
2.8%
Other values (3)3205
 
5.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number43399
73.1%
Other Punctuation11949
 
20.1%
Space Separator3983
 
6.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
012399
28.6%
110702
24.7%
26710
15.5%
73309
 
7.6%
83194
 
7.4%
62222
 
5.1%
31658
 
3.8%
91442
 
3.3%
51012
 
2.3%
4751
 
1.7%
Other Punctuation
ValueCountFrequency (%)
/7966
66.7%
:3983
33.3%
Space Separator
ValueCountFrequency (%)
3983
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common59331
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
012399
20.9%
110702
18.0%
/7966
13.4%
26710
11.3%
3983
 
6.7%
:3983
 
6.7%
73309
 
5.6%
83194
 
5.4%
62222
 
3.7%
31658
 
2.8%
Other values (3)3205
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII59331
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
012399
20.9%
110702
18.0%
/7966
13.4%
26710
11.3%
3983
 
6.7%
:3983
 
6.7%
73309
 
5.6%
83194
 
5.4%
62222
 
3.7%
31658
 
2.8%
Other values (3)3205
 
5.4%

return_shop_code
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size31.2 KiB
638
3983 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters11949
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row638
2nd row638
3rd row638
4th row638
5th row638

Common Values

ValueCountFrequency (%)
6383983
100.0%

Length

2022-09-12T22:17:05.907583image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-12T22:17:06.049495image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
6383983
100.0%

Most occurring characters

ValueCountFrequency (%)
63983
33.3%
33983
33.3%
83983
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number11949
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
63983
33.3%
33983
33.3%
83983
33.3%

Most occurring scripts

ValueCountFrequency (%)
Common11949
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
63983
33.3%
33983
33.3%
83983
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII11949
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
63983
33.3%
33983
33.3%
83983
33.3%

return_shop_name
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size31.2 KiB
新千歳空港店(ニコレンお客様大賞受賞店)
3983 

Length

Max length20
Median length20
Mean length20
Min length20

Characters and Unicode

Total characters79660
Distinct characters18
Distinct categories3 ?
Distinct scripts4 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row新千歳空港店(ニコレンお客様大賞受賞店)
2nd row新千歳空港店(ニコレンお客様大賞受賞店)
3rd row新千歳空港店(ニコレンお客様大賞受賞店)
4th row新千歳空港店(ニコレンお客様大賞受賞店)
5th row新千歳空港店(ニコレンお客様大賞受賞店)

Common Values

ValueCountFrequency (%)
新千歳空港店(ニコレンお客様大賞受賞店)3983
100.0%

Length

2022-09-12T22:17:06.299342image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-12T22:17:06.477233image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
新千歳空港店(ニコレンお客様大賞受賞店)3983
100.0%

Most occurring characters

ValueCountFrequency (%)
7966
 
10.0%
7966
 
10.0%
3983
 
5.0%
3983
 
5.0%
3983
 
5.0%
3983
 
5.0%
3983
 
5.0%
3983
 
5.0%
3983
 
5.0%
3983
 
5.0%
Other values (8)31864
40.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter71694
90.0%
Open Punctuation3983
 
5.0%
Close Punctuation3983
 
5.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
7966
 
11.1%
7966
 
11.1%
3983
 
5.6%
3983
 
5.6%
3983
 
5.6%
3983
 
5.6%
3983
 
5.6%
3983
 
5.6%
3983
 
5.6%
3983
 
5.6%
Other values (6)23898
33.3%
Open Punctuation
ValueCountFrequency (%)
3983
100.0%
Close Punctuation
ValueCountFrequency (%)
3983
100.0%

Most occurring scripts

ValueCountFrequency (%)
Han51779
65.0%
Katakana15932
 
20.0%
Common7966
 
10.0%
Hiragana3983
 
5.0%

Most frequent character per script

Han
ValueCountFrequency (%)
7966
15.4%
7966
15.4%
3983
7.7%
3983
7.7%
3983
7.7%
3983
7.7%
3983
7.7%
3983
7.7%
3983
7.7%
3983
7.7%
Katakana
ValueCountFrequency (%)
3983
25.0%
3983
25.0%
3983
25.0%
3983
25.0%
Common
ValueCountFrequency (%)
3983
50.0%
3983
50.0%
Hiragana
ValueCountFrequency (%)
3983
100.0%

Most occurring blocks

ValueCountFrequency (%)
CJK51779
65.0%
Katakana15932
 
20.0%
None7966
 
10.0%
Hiragana3983
 
5.0%

Most frequent character per block

CJK
ValueCountFrequency (%)
7966
15.4%
7966
15.4%
3983
7.7%
3983
7.7%
3983
7.7%
3983
7.7%
3983
7.7%
3983
7.7%
3983
7.7%
3983
7.7%
Katakana
ValueCountFrequency (%)
3983
25.0%
3983
25.0%
3983
25.0%
3983
25.0%
Hiragana
ValueCountFrequency (%)
3983
100.0%
None
ValueCountFrequency (%)
3983
50.0%
3983
50.0%

company_car_class_code
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct14
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.041174994
Minimum0
Maximum13
Zeros2159
Zeros (%)54.2%
Negative0
Negative (%)0.0%
Memory size31.2 KiB
2022-09-12T22:17:06.598926image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q34
95-th percentile8
Maximum13
Range13
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.775966278
Coefficient of variation (CV)1.359984463
Kurtosis0.07807595661
Mean2.041174994
Median Absolute Deviation (MAD)0
Skewness1.147558355
Sum8130
Variance7.705988775
MonotonicityNot monotonic
2022-09-12T22:17:06.741839image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
02159
54.2%
4638
 
16.0%
8326
 
8.2%
2257
 
6.5%
1225
 
5.6%
7216
 
5.4%
3119
 
3.0%
615
 
0.4%
99
 
0.2%
107
 
0.2%
Other values (4)12
 
0.3%
ValueCountFrequency (%)
02159
54.2%
1225
 
5.6%
2257
 
6.5%
3119
 
3.0%
4638
 
16.0%
53
 
0.1%
615
 
0.4%
7216
 
5.4%
8326
 
8.2%
99
 
0.2%
ValueCountFrequency (%)
133
 
0.1%
121
 
< 0.1%
115
 
0.1%
107
 
0.2%
99
 
0.2%
8326
8.2%
7216
 
5.4%
615
 
0.4%
53
 
0.1%
4638
16.0%

detail_car_class_code
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct38
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.4576952
Minimum0
Maximum37
Zeros134
Zeros (%)3.4%
Negative0
Negative (%)0.0%
Memory size31.2 KiB
2022-09-12T22:17:06.896744image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q14
median11
Q316
95-th percentile27
Maximum37
Range37
Interquartile range (IQR)12

Descriptive statistics

Standard deviation8.180175679
Coefficient of variation (CV)0.7139460016
Kurtosis-0.06953945836
Mean11.4576952
Median Absolute Deviation (MAD)6
Skewness0.8343231783
Sum45636
Variance66.91527414
MonotonicityNot monotonic
2022-09-12T22:17:07.088624image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
111039
26.1%
4616
15.5%
27326
 
8.2%
25213
 
5.3%
17203
 
5.1%
10178
 
4.5%
1169
 
4.2%
0134
 
3.4%
6132
 
3.3%
13129
 
3.2%
Other values (28)844
21.2%
ValueCountFrequency (%)
0134
 
3.4%
1169
 
4.2%
292
 
2.3%
3105
 
2.6%
4616
15.5%
535
 
0.9%
6132
 
3.3%
7127
 
3.2%
839
 
1.0%
9115
 
2.9%
ValueCountFrequency (%)
3718
0.5%
363
 
0.1%
353
 
0.1%
3414
0.4%
331
 
< 0.1%
321
 
< 0.1%
312
 
0.1%
305
 
0.1%
297
 
0.2%
289
0.2%

detail_car_class_name
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct38
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.4576952
Minimum0
Maximum37
Zeros134
Zeros (%)3.4%
Negative0
Negative (%)0.0%
Memory size31.2 KiB
2022-09-12T22:17:07.294503image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q14
median11
Q316
95-th percentile27
Maximum37
Range37
Interquartile range (IQR)12

Descriptive statistics

Standard deviation8.180175679
Coefficient of variation (CV)0.7139460016
Kurtosis-0.06953945836
Mean11.4576952
Median Absolute Deviation (MAD)6
Skewness0.8343231783
Sum45636
Variance66.91527414
MonotonicityNot monotonic
2022-09-12T22:17:07.510365image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
111039
26.1%
4616
15.5%
27326
 
8.2%
25213
 
5.3%
17203
 
5.1%
10178
 
4.5%
1169
 
4.2%
0134
 
3.4%
6132
 
3.3%
13129
 
3.2%
Other values (28)844
21.2%
ValueCountFrequency (%)
0134
 
3.4%
1169
 
4.2%
292
 
2.3%
3105
 
2.6%
4616
15.5%
535
 
0.9%
6132
 
3.3%
7127
 
3.2%
839
 
1.0%
9115
 
2.9%
ValueCountFrequency (%)
3718
0.5%
363
 
0.1%
353
 
0.1%
3414
0.4%
331
 
< 0.1%
321
 
< 0.1%
312
 
0.1%
305
 
0.1%
297
 
0.2%
289
0.2%

transmission
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size31.2 KiB
AT
3983 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters7966
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAT
2nd rowAT
3rd rowAT
4th rowAT
5th rowAT

Common Values

ValueCountFrequency (%)
AT3983
100.0%

Length

2022-09-12T22:17:07.694253image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-12T22:17:07.846159image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
at3983
100.0%

Most occurring characters

ValueCountFrequency (%)
A3983
50.0%
T3983
50.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter7966
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A3983
50.0%
T3983
50.0%

Most occurring scripts

ValueCountFrequency (%)
Latin7966
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A3983
50.0%
T3983
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII7966
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A3983
50.0%
T3983
50.0%

campaign
Categorical

HIGH CORRELATION
MISSING

Distinct24
Distinct (%)0.6%
Missing138
Missing (%)3.5%
Memory size31.2 KiB
★楽天限定★4WD・スタッドレスタイヤ標準装備でラクラクドライブ♪ 早い者勝ちの特別プラン◎楽しむなら、ニコニコレンタカーで!
1041 
【楽天らくらく得割り】1500店突破記念!ETC・ナビ付・ありがとうキャンペーン☆☆(暦日制)
843 
【楽天らくらく得割り】1,500店突破記念!早い者勝ち!最大33%OFF!ETC・ナビ付・ありがとうキャンペーン☆☆(時間制)
555 
【楽天限定!!】☆☆免責補償料込み・NOC免除キャンペーン☆☆
340 
☆◎新千歳空港店新規移転キャンペーン◎☆ 地域最大のニコニコレンタカー誕生!! 新店舗でさらにお客様の満足を目指します☆
328 
Other values (19)
738 

Length

Max length79
Median length68
Mean length56.37477243
Min length13

Characters and Unicode

Total characters216761
Distinct characters226
Distinct categories10 ?
Distinct scripts5 ?
Distinct blocks8 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.1%

Sample

1st row★楽天限定★4WD・スタッドレスタイヤ標準装備でラクラクドライブ♪ 早い者勝ちの特別プラン◎楽しむなら、ニコニコレンタカーで!
2nd row★楽天限定★4WD・スタッドレスタイヤ標準装備でラクラクドライブ♪ 早い者勝ちの特別プラン◎楽しむなら、ニコニコレンタカーで!
3rd row★楽天限定★4WD・スタッドレスタイヤ標準装備でラクラクドライブ♪ 早い者勝ちの特別プラン◎楽しむなら、ニコニコレンタカーで!
4th row★楽天限定★4WD・スタッドレスタイヤ標準装備でラクラクドライブ♪ 早い者勝ちの特別プラン◎楽しむなら、ニコニコレンタカーで!
5th row★楽天限定★4WD・スタッドレスタイヤ標準装備でラクラクドライブ♪ 早い者勝ちの特別プラン◎楽しむなら、ニコニコレンタカーで!

Common Values

ValueCountFrequency (%)
★楽天限定★4WD・スタッドレスタイヤ標準装備でラクラクドライブ♪ 早い者勝ちの特別プラン◎楽しむなら、ニコニコレンタカーで!1041
26.1%
【楽天らくらく得割り】1500店突破記念!ETC・ナビ付・ありがとうキャンペーン☆☆(暦日制)843
21.2%
【楽天らくらく得割り】1,500店突破記念!早い者勝ち!最大33%OFF!ETC・ナビ付・ありがとうキャンペーン☆☆(時間制)555
13.9%
【楽天限定!!】☆☆免責補償料込み・NOC免除キャンペーン☆☆340
 
8.5%
☆◎新千歳空港店新規移転キャンペーン◎☆ 地域最大のニコニコレンタカー誕生!! 新店舗でさらにお客様の満足を目指します☆ 328
 
8.2%
【2016年6月購入】★ピカピカ新車プラン!★ 早いもの勝ちの特別プラン◎下ろし立ての新車がニコニコレンタカーで乗れる!203
 
5.1%
☆ハイブリッド限定プラン☆ 燃費のいいプリウス・アクアなどで快適ドライブ! ★スタットレスタイヤ標準装備★ 台数限定プランです。お早目にご予約下さい!!182
 
4.6%
【ポイント10倍】★楽天限定★ナビ・ETC標準装備でポイント10倍♪ 早い者勝ちの特別プラン◎楽しむなら、ニコニコレンタカーで!118
 
3.0%
【宿泊予約者限定!!】1500店突破記念!早い者勝ち!ETC・ナビ付・ありがとうキャンペーン☆☆83
 
2.1%
【楽天レンタカー10周年記念】最大55%OFF!割引キャンペーン26
 
0.7%
Other values (14)126
 
3.2%
(Missing)138
 
3.5%

Length

2022-09-12T22:17:07.995066image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
早い者勝ちの特別プラン◎楽しむなら、ニコニコレンタカーで!1159
17.9%
★楽天限定★4wd・スタッドレスタイヤ標準装備でラクラクドライブ♪1054
16.3%
【楽天らくらく得割り】1500店突破記念!etc・ナビ付・ありがとうキャンペーン☆☆(暦日制)843
13.0%
【楽天らくらく得割り】1,500店突破記念!早い者勝ち!最大33%off!etc・ナビ付・ありがとうキャンペーン☆☆(時間制555
8.6%
【楽天限定!!】☆☆免責補償料込み・noc免除キャンペーン☆☆340
 
5.2%
☆◎新千歳空港店新規移転キャンペーン◎☆328
 
5.1%
地域最大のニコニコレンタカー誕生!!328
 
5.1%
新店舗でさらにお客様の満足を目指します☆328
 
5.1%
【2016年6月購入】★ピカピカ新車プラン!★203
 
3.1%
早いもの勝ちの特別プラン◎下ろし立ての新車がニコニコレンタカーで乗れる!203
 
3.1%
Other values (28)1136
17.5%

Most occurring characters

ValueCountFrequency (%)
8398
 
3.9%
6189
 
2.9%
5803
 
2.7%
5342
 
2.5%
4660
 
2.1%
4296
 
2.0%
4236
 
2.0%
4156
 
1.9%
4068
 
1.9%
03547
 
1.6%
Other values (216)166066
76.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter153389
70.8%
Other Punctuation13819
 
6.4%
Other Symbol12221
 
5.6%
Uppercase Letter10100
 
4.7%
Decimal Number9894
 
4.6%
Control5916
 
2.7%
Modifier Letter4068
 
1.9%
Close Punctuation3654
 
1.7%
Open Punctuation3654
 
1.7%
Space Separator46
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
8398
 
5.5%
5342
 
3.5%
4296
 
2.8%
4236
 
2.8%
4156
 
2.7%
3477
 
2.3%
3468
 
2.3%
3239
 
2.1%
3000
 
2.0%
2984
 
1.9%
Other values (163)110793
72.2%
Uppercase Letter
ValueCountFrequency (%)
C1939
19.2%
E1636
16.2%
T1608
15.9%
F1277
12.6%
W1054
10.4%
D1054
10.4%
O969
9.6%
N359
 
3.6%
L47
 
0.5%
A47
 
0.5%
Other values (9)110
 
1.1%
Other Punctuation
ValueCountFrequency (%)
6189
44.8%
4660
33.7%
1210
 
8.8%
601
 
4.3%
,555
 
4.0%
!369
 
2.7%
182
 
1.3%
%28
 
0.2%
/17
 
0.1%
8
 
0.1%
Decimal Number
ValueCountFrequency (%)
03547
35.9%
11950
19.7%
51607
16.2%
31118
 
11.3%
41054
 
10.7%
6406
 
4.1%
2203
 
2.1%
9
 
0.1%
Other Symbol
ValueCountFrequency (%)
5803
47.5%
3196
26.2%
2018
 
16.5%
1174
 
9.6%
30
 
0.2%
Close Punctuation
ValueCountFrequency (%)
2256
61.7%
843
 
23.1%
)555
 
15.2%
Open Punctuation
ValueCountFrequency (%)
2256
61.7%
843
 
23.1%
(555
 
15.2%
Control
ValueCountFrequency (%)
2958
50.0%
2958
50.0%
Space Separator
ValueCountFrequency (%)
31
67.4%
 15
32.6%
Modifier Letter
ValueCountFrequency (%)
4068
100.0%

Most occurring scripts

ValueCountFrequency (%)
Katakana59140
27.3%
Han58877
27.2%
Common53272
24.6%
Hiragana35372
16.3%
Latin10100
 
4.7%

Most frequent character per script

Han
ValueCountFrequency (%)
4156
 
7.1%
2984
 
5.1%
2182
 
3.7%
2141
 
3.6%
2000
 
3.4%
1982
 
3.4%
1982
 
3.4%
1884
 
3.2%
1528
 
2.6%
1528
 
2.6%
Other values (93)36510
62.0%
Katakana
ValueCountFrequency (%)
8398
14.2%
5342
 
9.0%
4236
 
7.2%
3477
 
5.9%
3468
 
5.9%
3000
 
5.1%
2895
 
4.9%
2716
 
4.6%
2472
 
4.2%
2337
 
4.0%
Other values (29)20799
35.2%
Common
ValueCountFrequency (%)
6189
11.6%
5803
 
10.9%
4660
 
8.7%
4068
 
7.6%
03547
 
6.7%
3196
 
6.0%
2958
 
5.6%
2958
 
5.6%
2256
 
4.2%
2256
 
4.2%
Other values (24)15381
28.9%
Hiragana
ValueCountFrequency (%)
4296
12.1%
3239
 
9.2%
2903
 
8.2%
2796
 
7.9%
2621
 
7.4%
2546
 
7.2%
2000
 
5.7%
1703
 
4.8%
1684
 
4.8%
1496
 
4.2%
Other values (21)10088
28.5%
Latin
ValueCountFrequency (%)
C1939
19.2%
E1636
16.2%
T1608
15.9%
F1277
12.6%
W1054
10.4%
D1054
10.4%
O969
9.6%
N359
 
3.6%
L47
 
0.5%
A47
 
0.5%
Other values (9)110
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
Katakana67868
31.3%
CJK58877
27.2%
Hiragana35372
16.3%
ASCII27991
12.9%
None14424
 
6.7%
Misc Symbols10173
 
4.7%
Geometric Shapes2048
 
0.9%
Punctuation8
 
< 0.1%

Most frequent character per block

Katakana
ValueCountFrequency (%)
8398
 
12.4%
5342
 
7.9%
4660
 
6.9%
4236
 
6.2%
4068
 
6.0%
3477
 
5.1%
3468
 
5.1%
3000
 
4.4%
2895
 
4.3%
2716
 
4.0%
Other values (31)25608
37.7%
None
ValueCountFrequency (%)
6189
42.9%
2256
 
15.6%
2256
 
15.6%
1210
 
8.4%
843
 
5.8%
843
 
5.8%
601
 
4.2%
182
 
1.3%
 15
 
0.1%
9
 
0.1%
Other values (5)20
 
0.1%
Misc Symbols
ValueCountFrequency (%)
5803
57.0%
3196
31.4%
1174
 
11.5%
Hiragana
ValueCountFrequency (%)
4296
12.1%
3239
 
9.2%
2903
 
8.2%
2796
 
7.9%
2621
 
7.4%
2546
 
7.2%
2000
 
5.7%
1703
 
4.8%
1684
 
4.8%
1496
 
4.2%
Other values (21)10088
28.5%
CJK
ValueCountFrequency (%)
4156
 
7.1%
2984
 
5.1%
2182
 
3.7%
2141
 
3.6%
2000
 
3.4%
1982
 
3.4%
1982
 
3.4%
1884
 
3.2%
1528
 
2.6%
1528
 
2.6%
Other values (93)36510
62.0%
ASCII
ValueCountFrequency (%)
03547
12.7%
2958
10.6%
2958
10.6%
11950
 
7.0%
C1939
 
6.9%
E1636
 
5.8%
T1608
 
5.7%
51607
 
5.7%
F1277
 
4.6%
31118
 
4.0%
Other values (20)7393
26.4%
Geometric Shapes
ValueCountFrequency (%)
2018
98.5%
30
 
1.5%
Punctuation
ValueCountFrequency (%)
8
100.0%

car_attribute
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)1.0%
Missing3777
Missing (%)94.8%
Memory size31.2 KiB
最安値保証, 登録1年未満
203 
登録1年未満
 
3

Length

Max length13
Median length13
Mean length12.89805825
Min length6

Characters and Unicode

Total characters2657
Distinct characters13
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row最安値保証, 登録1年未満
2nd row最安値保証, 登録1年未満
3rd row最安値保証, 登録1年未満
4th row最安値保証, 登録1年未満
5th row最安値保証, 登録1年未満

Common Values

ValueCountFrequency (%)
最安値保証, 登録1年未満203
 
5.1%
登録1年未満3
 
0.1%
(Missing)3777
94.8%

Length

2022-09-12T22:17:08.160351image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-12T22:17:08.393208image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
登録1年未満206
50.4%
最安値保証203
49.6%

Most occurring characters

ValueCountFrequency (%)
206
 
7.8%
206
 
7.8%
1206
 
7.8%
206
 
7.8%
206
 
7.8%
206
 
7.8%
203
 
7.6%
203
 
7.6%
203
 
7.6%
203
 
7.6%
Other values (3)609
22.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter2045
77.0%
Decimal Number206
 
7.8%
Other Punctuation203
 
7.6%
Space Separator203
 
7.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
206
10.1%
206
10.1%
206
10.1%
206
10.1%
206
10.1%
203
9.9%
203
9.9%
203
9.9%
203
9.9%
203
9.9%
Decimal Number
ValueCountFrequency (%)
1206
100.0%
Other Punctuation
ValueCountFrequency (%)
,203
100.0%
Space Separator
ValueCountFrequency (%)
203
100.0%

Most occurring scripts

ValueCountFrequency (%)
Han2045
77.0%
Common612
 
23.0%

Most frequent character per script

Han
ValueCountFrequency (%)
206
10.1%
206
10.1%
206
10.1%
206
10.1%
206
10.1%
203
9.9%
203
9.9%
203
9.9%
203
9.9%
203
9.9%
Common
ValueCountFrequency (%)
1206
33.7%
,203
33.2%
203
33.2%

Most occurring blocks

ValueCountFrequency (%)
CJK2045
77.0%
ASCII612
 
23.0%

Most frequent character per block

CJK
ValueCountFrequency (%)
206
10.1%
206
10.1%
206
10.1%
206
10.1%
206
10.1%
203
9.9%
203
9.9%
203
9.9%
203
9.9%
203
9.9%
ASCII
ValueCountFrequency (%)
1206
33.7%
,203
33.2%
203
33.2%

basic_price
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct467
Distinct (%)11.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13265.56113
Minimum400
Maximum146300
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.2 KiB
2022-09-12T22:17:08.566286image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum400
5-th percentile3700
Q16500
median9800
Q315800
95-th percentile34980
Maximum146300
Range145900
Interquartile range (IQR)9300

Descriptive statistics

Standard deviation11365.3404
Coefficient of variation (CV)0.8567553443
Kurtosis16.50210784
Mean13265.56113
Median Absolute Deviation (MAD)3800
Skewness3.111351504
Sum52836730
Variance129170962.4
MonotonicityNot monotonic
2022-09-12T22:17:08.757166image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6000148
 
3.7%
8000131
 
3.3%
6500120
 
3.0%
900088
 
2.2%
1000084
 
2.1%
750081
 
2.0%
1200078
 
2.0%
450075
 
1.9%
400073
 
1.8%
700068
 
1.7%
Other values (457)3037
76.2%
ValueCountFrequency (%)
4002
 
0.1%
10001
 
< 0.1%
10201
 
< 0.1%
12001
 
< 0.1%
24006
 
0.2%
25009
 
0.2%
270027
0.7%
28002
 
0.1%
29007
 
0.2%
300059
1.5%
ValueCountFrequency (%)
1463001
< 0.1%
1400001
< 0.1%
960001
< 0.1%
950001
< 0.1%
865001
< 0.1%
860001
< 0.1%
840001
< 0.1%
800002
0.1%
780002
0.1%
777001
< 0.1%

drop_off_fee
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size31.2 KiB
0
3983 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3983
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
03983
100.0%

Length

2022-09-12T22:17:08.937057image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-12T22:17:09.072972image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
03983
100.0%

Most occurring characters

ValueCountFrequency (%)
03983
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3983
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03983
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common3983
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
03983
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII3983
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03983
100.0%

options_total_fee
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct49
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2782.318855
Minimum0
Maximum28080
Zeros1005
Zeros (%)25.2%
Negative0
Negative (%)0.0%
Memory size31.2 KiB
2022-09-12T22:17:09.209237image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2160
Q34320
95-th percentile6480
Maximum28080
Range28080
Interquartile range (IQR)4320

Descriptive statistics

Standard deviation2585.639086
Coefficient of variation (CV)0.929310845
Kurtosis8.833531549
Mean2782.318855
Median Absolute Deviation (MAD)2160
Skewness1.91525851
Sum11081976
Variance6685529.484
MonotonicityNot monotonic
2022-09-12T22:17:09.411950image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
01005
25.2%
3240883
22.2%
2160650
16.3%
4320345
 
8.7%
1080215
 
5.4%
4860200
 
5.0%
6480177
 
4.4%
5400137
 
3.4%
1620119
 
3.0%
810039
 
1.0%
Other values (39)213
 
5.3%
ValueCountFrequency (%)
01005
25.2%
32414
 
0.4%
3501
 
< 0.1%
54010
 
0.3%
8641
 
< 0.1%
1080215
 
5.4%
14041
 
< 0.1%
14301
 
< 0.1%
1620119
 
3.0%
19442
 
0.1%
ValueCountFrequency (%)
280801
 
< 0.1%
243001
 
< 0.1%
221401
 
< 0.1%
210601
 
< 0.1%
197641
 
< 0.1%
194402
0.1%
183602
0.1%
178202
0.1%
162003
0.1%
151204
0.1%

night_fee_pickup
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size31.2 KiB
0
3983 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3983
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
03983
100.0%

Length

2022-09-12T22:17:09.573848image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-12T22:17:09.709764image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
03983
100.0%

Most occurring characters

ValueCountFrequency (%)
03983
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3983
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03983
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common3983
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
03983
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII3983
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03983
100.0%

night_fee_return
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size31.2 KiB
0
3983 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3983
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
03983
100.0%

Length

2022-09-12T22:17:09.823581image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-12T22:17:09.999473image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
03983
100.0%

Most occurring characters

ValueCountFrequency (%)
03983
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3983
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03983
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common3983
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
03983
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII3983
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03983
100.0%

subtotal_amount
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1101
Distinct (%)27.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16033.38012
Minimum100
Maximum149720
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.2 KiB
2022-09-12T22:17:10.138008image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile4500
Q18480
median12460
Q319300
95-th percentile39445
Maximum149720
Range149620
Interquartile range (IQR)10820

Descriptive statistics

Standard deviation12317.86468
Coefficient of variation (CV)0.7682637464
Kurtosis14.5076778
Mean16033.38012
Median Absolute Deviation (MAD)4800
Skewness2.893315289
Sum63860953
Variance151729790.2
MonotonicityNot monotonic
2022-09-12T22:17:10.333887image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
816052
 
1.3%
924048
 
1.2%
974047
 
1.2%
666043
 
1.1%
1124041
 
1.0%
408041
 
1.0%
1224040
 
1.0%
600037
 
0.9%
1324033
 
0.8%
1016033
 
0.8%
Other values (1091)3568
89.6%
ValueCountFrequency (%)
1001
 
< 0.1%
20201
 
< 0.1%
23441
 
< 0.1%
25001
 
< 0.1%
27006
0.2%
28001
 
< 0.1%
30007
0.2%
31601
 
< 0.1%
32001
 
< 0.1%
32401
 
< 0.1%
ValueCountFrequency (%)
1497201
< 0.1%
1463001
< 0.1%
1060801
< 0.1%
1050601
< 0.1%
1043001
< 0.1%
1031001
< 0.1%
960001
< 0.1%
875601
< 0.1%
872641
< 0.1%
868241
< 0.1%

coupon
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct14
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean184.5844841
Minimum0
Maximum4000
Zeros3481
Zeros (%)87.4%
Negative0
Negative (%)0.0%
Memory size31.2 KiB
2022-09-12T22:17:10.502783image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1500
Maximum4000
Range4000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation521.9235487
Coefficient of variation (CV)2.82755916
Kurtosis8.200800558
Mean184.5844841
Median Absolute Deviation (MAD)0
Skewness2.919564193
Sum735200
Variance272404.1907
MonotonicityNot monotonic
2022-09-12T22:17:10.649692image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
03481
87.4%
1500167
 
4.2%
1000132
 
3.3%
200098
 
2.5%
70033
 
0.8%
160020
 
0.5%
250017
 
0.4%
80016
 
0.4%
30009
 
0.2%
5003
 
0.1%
Other values (4)7
 
0.2%
ValueCountFrequency (%)
03481
87.4%
5003
 
0.1%
70033
 
0.8%
80016
 
0.4%
1000132
 
3.3%
12003
 
0.1%
1500167
 
4.2%
160020
 
0.5%
200098
 
2.5%
250017
 
0.4%
ValueCountFrequency (%)
40001
 
< 0.1%
35002
 
0.1%
32001
 
< 0.1%
30009
 
0.2%
250017
 
0.4%
200098
2.5%
160020
 
0.5%
1500167
4.2%
12003
 
0.1%
1000132
3.3%

point
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct113
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean368.9480291
Minimum0
Maximum30000
Zeros3500
Zeros (%)87.9%
Negative0
Negative (%)0.0%
Memory size31.2 KiB
2022-09-12T22:17:10.843575image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2000
Maximum30000
Range30000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1719.327796
Coefficient of variation (CV)4.660081258
Kurtosis84.45095232
Mean368.9480291
Median Absolute Deviation (MAD)0
Skewness7.886528995
Sum1469520
Variance2956088.069
MonotonicityNot monotonic
2022-09-12T22:17:11.031298image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
03500
87.9%
100034
 
0.9%
50030
 
0.8%
20030
 
0.8%
10028
 
0.7%
200028
 
0.7%
40019
 
0.5%
80018
 
0.5%
60017
 
0.4%
30017
 
0.4%
Other values (103)262
 
6.6%
ValueCountFrequency (%)
03500
87.9%
10028
 
0.7%
20030
 
0.8%
30017
 
0.4%
40019
 
0.5%
50030
 
0.8%
60017
 
0.4%
7009
 
0.2%
80018
 
0.5%
90011
 
0.3%
ValueCountFrequency (%)
300002
0.1%
228601
< 0.1%
200001
< 0.1%
184801
< 0.1%
177601
< 0.1%
171201
< 0.1%
166601
< 0.1%
160001
< 0.1%
157001
< 0.1%
155201
< 0.1%

total_amount
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct1136
Distinct (%)28.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12329.9769
Minimum0
Maximum149720
Zeros790
Zeros (%)19.8%
Negative0
Negative (%)0.0%
Memory size31.2 KiB
2022-09-12T22:17:11.220746image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14360
median9960
Q316370
95-th percentile35400
Maximum149720
Range149720
Interquartile range (IQR)12010

Descriptive statistics

Standard deviation12594.45509
Coefficient of variation (CV)1.021450015
Kurtosis13.79674288
Mean12329.9769
Median Absolute Deviation (MAD)5880
Skewness2.637500222
Sum49110298
Variance158620299.1
MonotonicityNot monotonic
2022-09-12T22:17:11.397871image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0790
 
19.8%
816040
 
1.0%
974034
 
0.9%
666033
 
0.8%
1124030
 
0.8%
924028
 
0.7%
408028
 
0.7%
1224026
 
0.7%
966025
 
0.6%
766025
 
0.6%
Other values (1126)2924
73.4%
ValueCountFrequency (%)
0790
19.8%
501
 
< 0.1%
601
 
< 0.1%
801
 
< 0.1%
2001
 
< 0.1%
2801
 
< 0.1%
3001
 
< 0.1%
3601
 
< 0.1%
3801
 
< 0.1%
4801
 
< 0.1%
ValueCountFrequency (%)
1497201
< 0.1%
1463001
< 0.1%
1060801
< 0.1%
1050601
< 0.1%
1043001
< 0.1%
1026001
< 0.1%
960001
< 0.1%
875601
< 0.1%
860001
< 0.1%
808601
< 0.1%

taxable_amount
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1101
Distinct (%)27.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16033.38012
Minimum100
Maximum149720
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.2 KiB
2022-09-12T22:17:11.591758image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile4500
Q18480
median12460
Q319300
95-th percentile39445
Maximum149720
Range149620
Interquartile range (IQR)10820

Descriptive statistics

Standard deviation12317.86468
Coefficient of variation (CV)0.7682637464
Kurtosis14.5076778
Mean16033.38012
Median Absolute Deviation (MAD)4800
Skewness2.893315289
Sum63860953
Variance151729790.2
MonotonicityNot monotonic
2022-09-12T22:17:11.772230image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
816052
 
1.3%
924048
 
1.2%
974047
 
1.2%
666043
 
1.1%
1124041
 
1.0%
408041
 
1.0%
1224040
 
1.0%
600037
 
0.9%
1324033
 
0.8%
1016033
 
0.8%
Other values (1091)3568
89.6%
ValueCountFrequency (%)
1001
 
< 0.1%
20201
 
< 0.1%
23441
 
< 0.1%
25001
 
< 0.1%
27006
0.2%
28001
 
< 0.1%
30007
0.2%
31601
 
< 0.1%
32001
 
< 0.1%
32401
 
< 0.1%
ValueCountFrequency (%)
1497201
< 0.1%
1463001
< 0.1%
1060801
< 0.1%
1050601
< 0.1%
1043001
< 0.1%
1031001
< 0.1%
960001
< 0.1%
875601
< 0.1%
872641
< 0.1%
868241
< 0.1%

non_taxable_amount
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size31.2 KiB
0
3983 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3983
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
03983
100.0%

Length

2022-09-12T22:17:11.940129image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-12T22:17:12.081042image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
03983
100.0%

Most occurring characters

ValueCountFrequency (%)
03983
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3983
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03983
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common3983
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
03983
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII3983
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03983
100.0%

cancel_fee
Real number (ℝ≥0)

ZEROS

Distinct38
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean66.17875973
Minimum0
Maximum4300
Zeros3906
Zeros (%)98.1%
Negative0
Negative (%)0.0%
Memory size31.2 KiB
2022-09-12T22:17:12.215957image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum4300
Range4300
Interquartile range (IQR)0

Descriptive statistics

Standard deviation493.9265158
Coefficient of variation (CV)7.463520287
Kurtosis59.2149404
Mean66.17875973
Median Absolute Deviation (MAD)0
Skewness7.704281659
Sum263590
Variance243963.403
MonotonicityNot monotonic
2022-09-12T22:17:12.381016image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
03906
98.1%
430035
 
0.9%
34773
 
0.1%
12242
 
0.1%
21002
 
0.1%
26882
 
0.1%
22082
 
0.1%
12001
 
< 0.1%
38301
 
< 0.1%
21401
 
< 0.1%
Other values (28)28
 
0.7%
ValueCountFrequency (%)
03906
98.1%
501
 
< 0.1%
12001
 
< 0.1%
12242
 
0.1%
13501
 
< 0.1%
18001
 
< 0.1%
18601
 
< 0.1%
19501
 
< 0.1%
20401
 
< 0.1%
20431
 
< 0.1%
ValueCountFrequency (%)
430035
0.9%
40801
 
< 0.1%
40681
 
< 0.1%
39721
 
< 0.1%
39371
 
< 0.1%
39001
 
< 0.1%
38301
 
< 0.1%
37801
 
< 0.1%
37381
 
< 0.1%
36721
 
< 0.1%

memo
Categorical

HIGH CARDINALITY
HIGH CORRELATION
MISSING
UNIFORM

Distinct86
Distinct (%)96.6%
Missing3894
Missing (%)97.8%
Memory size31.2 KiB
チェックイン:2017/09/13 17:00 チェックアウト:2017/09/14
 
2
チェックイン:2017/05/14 20:00 チェックアウト:2017/05/15
 
2
チェックイン:2018/02/02 15:00 チェックアウト:2018/02/04
 
2
チェックイン:2018/04/23 14:00 チェックアウト:2018/04/25
 
1
チェックイン:2018/03/31 15:00 チェックアウト:2018/04/01
 
1
Other values (81)
81 

Length

Max length62
Median length43
Mean length42.94382022
Min length19

Characters and Unicode

Total characters3822
Distinct characters83
Distinct categories7 ?
Distinct scripts5 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique83 ?
Unique (%)93.3%

Sample

1st rowチェックイン:2016/03/14 17:00 チェックアウト:2016/03/15
2nd rowチェックイン:2016/02/27 23:00 チェックアウト:2016/02/29
3rd row出発:5/26 15:30 帰着:6/5 14:00に変更
4th rowチェックイン:2016/03/18 20:30 チェックアウト:2016/03/19
5th rowチェックイン:2016/03/11 20:30 チェックアウト:2016/03/12

Common Values

ValueCountFrequency (%)
チェックイン:2017/09/13 17:00 チェックアウト:2017/09/142
 
0.1%
チェックイン:2017/05/14 20:00 チェックアウト:2017/05/152
 
0.1%
チェックイン:2018/02/02 15:00 チェックアウト:2018/02/042
 
0.1%
チェックイン:2018/04/23 14:00 チェックアウト:2018/04/251
 
< 0.1%
チェックイン:2018/03/31 15:00 チェックアウト:2018/04/011
 
< 0.1%
チェックイン:2018/02/15 18:00 チェックアウト:2018/02/191
 
< 0.1%
チェックイン:2018/02/24 17:00 チェックアウト:2018/02/261
 
< 0.1%
チェックイン:2018/03/15 17:00 チェックアウト:2018/03/161
 
< 0.1%
チェックイン:2018/02/06 20:00 チェックアウト:2018/02/081
 
< 0.1%
チェックイン:2018/04/16 14:00 チェックアウト:2018/04/181
 
< 0.1%
Other values (76)76
 
1.9%
(Missing)3894
97.8%

Length

2022-09-12T22:17:12.540919image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
15:0015
 
5.6%
17:0011
 
4.1%
19:009
 
3.3%
20:008
 
3.0%
18:007
 
2.6%
21:006
 
2.2%
14:005
 
1.9%
17:305
 
1.9%
16:005
 
1.9%
20:303
 
1.1%
Other values (164)195
72.5%

Most occurring characters

ValueCountFrequency (%)
0552
14.4%
1349
 
9.1%
/345
 
9.0%
2304
 
8.0%
174
 
4.6%
166
 
4.3%
166
 
4.3%
166
 
4.3%
166
 
4.3%
7117
 
3.1%
Other values (73)1317
34.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1743
45.6%
Other Letter1172
30.7%
Other Punctuation612
 
16.0%
Control182
 
4.8%
Space Separator109
 
2.9%
Uppercase Letter3
 
0.1%
Currency Symbol1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
166
14.2%
166
14.2%
166
14.2%
166
14.2%
83
7.1%
83
7.1%
83
7.1%
83
7.1%
83
7.1%
6
 
0.5%
Other values (51)87
7.4%
Decimal Number
ValueCountFrequency (%)
0552
31.7%
1349
20.0%
2304
17.4%
7117
 
6.7%
6101
 
5.8%
898
 
5.6%
370
 
4.0%
559
 
3.4%
447
 
2.7%
946
 
2.6%
Other Punctuation
ValueCountFrequency (%)
/345
56.4%
174
28.4%
:90
 
14.7%
2
 
0.3%
1
 
0.2%
Space Separator
ValueCountFrequency (%)
92
84.4%
 17
 
15.6%
Control
ValueCountFrequency (%)
91
50.0%
91
50.0%
Uppercase Letter
ValueCountFrequency (%)
J2
66.7%
P1
33.3%
Currency Symbol
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common2647
69.3%
Katakana1079
28.2%
Han65
 
1.7%
Hiragana28
 
0.7%
Latin3
 
0.1%

Most frequent character per script

Han
ValueCountFrequency (%)
6
 
9.2%
5
 
7.7%
便5
 
7.7%
4
 
6.2%
4
 
6.2%
4
 
6.2%
4
 
6.2%
4
 
6.2%
3
 
4.6%
2
 
3.1%
Other values (24)24
36.9%
Common
ValueCountFrequency (%)
0552
20.9%
1349
13.2%
/345
13.0%
2304
11.5%
174
 
6.6%
7117
 
4.4%
6101
 
3.8%
898
 
3.7%
92
 
3.5%
91
 
3.4%
Other values (10)424
16.0%
Hiragana
ValueCountFrequency (%)
3
 
10.7%
3
 
10.7%
3
 
10.7%
2
 
7.1%
2
 
7.1%
2
 
7.1%
2
 
7.1%
1
 
3.6%
1
 
3.6%
1
 
3.6%
Other values (8)8
28.6%
Katakana
ValueCountFrequency (%)
166
15.4%
166
15.4%
166
15.4%
166
15.4%
83
7.7%
83
7.7%
83
7.7%
83
7.7%
83
7.7%
Latin
ValueCountFrequency (%)
J2
66.7%
P1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII2455
64.2%
Katakana1079
28.2%
None195
 
5.1%
CJK65
 
1.7%
Hiragana28
 
0.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0552
22.5%
1349
14.2%
/345
14.1%
2304
12.4%
7117
 
4.8%
6101
 
4.1%
898
 
4.0%
92
 
3.7%
91
 
3.7%
91
 
3.7%
Other values (7)315
12.8%
None
ValueCountFrequency (%)
174
89.2%
 17
 
8.7%
2
 
1.0%
1
 
0.5%
1
 
0.5%
Katakana
ValueCountFrequency (%)
166
15.4%
166
15.4%
166
15.4%
166
15.4%
83
7.7%
83
7.7%
83
7.7%
83
7.7%
83
7.7%
CJK
ValueCountFrequency (%)
6
 
9.2%
5
 
7.7%
便5
 
7.7%
4
 
6.2%
4
 
6.2%
4
 
6.2%
4
 
6.2%
4
 
6.2%
3
 
4.6%
2
 
3.1%
Other values (24)24
36.9%
Hiragana
ValueCountFrequency (%)
3
 
10.7%
3
 
10.7%
3
 
10.7%
2
 
7.1%
2
 
7.1%
2
 
7.1%
2
 
7.1%
1
 
3.6%
1
 
3.6%
1
 
3.6%
Other values (8)8
28.6%

options
Unsupported

REJECTED
UNSUPPORTED

Missing0
Missing (%)0.0%
Memory size31.2 KiB

cancellation_reason
Categorical

HIGH CORRELATION
MISSING

Distinct9
Distinct (%)14.5%
Missing3921
Missing (%)98.4%
Memory size31.2 KiB
ノーチャージキャンセル
54 
免責補償非加入でとご本人から連絡きたため
 
1
日時変更の為
 
1
返却日時変更の為
 
1
日程変更の為
 
1
Other values (4)
 
4

Length

Max length20
Median length11
Mean length11.03225806
Min length6

Characters and Unicode

Total characters684
Distinct characters65
Distinct categories5 ?
Distinct scripts5 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)12.9%

Sample

1st rowノーチャージキャンセル
2nd rowノーチャージキャンセル
3rd rowノーチャージキャンセル
4th rowノーチャージキャンセル
5th rowノーチャージキャンセル

Common Values

ValueCountFrequency (%)
ノーチャージキャンセル54
 
1.4%
免責補償非加入でとご本人から連絡きたため1
 
< 0.1%
日時変更の為1
 
< 0.1%
返却日時変更の為1
 
< 0.1%
日程変更の為1
 
< 0.1%
21歳未満のため1
 
< 0.1%
ETCカード利用なしの為1
 
< 0.1%
ETCカード不要だった為1
 
< 0.1%
悪天候で欠航の為、次の日の出発に変更1
 
< 0.1%
(Missing)3921
98.4%

Length

2022-09-12T22:17:12.694785image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-12T22:17:12.871183image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
ノーチャージキャンセル54
87.1%
免責補償非加入でとご本人から連絡きたため1
 
1.6%
日時変更の為1
 
1.6%
返却日時変更の為1
 
1.6%
日程変更の為1
 
1.6%
21歳未満のため1
 
1.6%
etcカード利用なしの為1
 
1.6%
etcカード不要だった為1
 
1.6%
悪天候で欠航の為、次の日の出発に変更1
 
1.6%

Most occurring characters

ValueCountFrequency (%)
110
16.1%
108
15.8%
54
7.9%
54
7.9%
54
7.9%
54
7.9%
54
7.9%
54
7.9%
54
7.9%
8
 
1.2%
Other values (55)80
11.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter565
82.6%
Modifier Letter110
 
16.1%
Uppercase Letter6
 
0.9%
Decimal Number2
 
0.3%
Other Punctuation1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
108
19.1%
54
9.6%
54
9.6%
54
9.6%
54
9.6%
54
9.6%
54
9.6%
54
9.6%
8
 
1.4%
6
 
1.1%
Other values (48)65
11.5%
Uppercase Letter
ValueCountFrequency (%)
E2
33.3%
T2
33.3%
C2
33.3%
Decimal Number
ValueCountFrequency (%)
21
50.0%
11
50.0%
Modifier Letter
ValueCountFrequency (%)
110
100.0%
Other Punctuation
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Katakana490
71.6%
Common113
 
16.5%
Han49
 
7.2%
Hiragana26
 
3.8%
Latin6
 
0.9%

Most frequent character per script

Han
ValueCountFrequency (%)
6
 
12.2%
4
 
8.2%
4
 
8.2%
4
 
8.2%
2
 
4.1%
1
 
2.0%
1
 
2.0%
1
 
2.0%
1
 
2.0%
1
 
2.0%
Other values (24)24
49.0%
Hiragana
ValueCountFrequency (%)
8
30.8%
4
15.4%
2
 
7.7%
2
 
7.7%
1
 
3.8%
1
 
3.8%
1
 
3.8%
1
 
3.8%
1
 
3.8%
1
 
3.8%
Other values (4)4
15.4%
Katakana
ValueCountFrequency (%)
108
22.0%
54
11.0%
54
11.0%
54
11.0%
54
11.0%
54
11.0%
54
11.0%
54
11.0%
2
 
0.4%
2
 
0.4%
Common
ValueCountFrequency (%)
110
97.3%
1
 
0.9%
21
 
0.9%
11
 
0.9%
Latin
ValueCountFrequency (%)
E2
33.3%
T2
33.3%
C2
33.3%

Most occurring blocks

ValueCountFrequency (%)
Katakana600
87.7%
CJK49
 
7.2%
Hiragana26
 
3.8%
ASCII8
 
1.2%
None1
 
0.1%

Most frequent character per block

Katakana
ValueCountFrequency (%)
110
18.3%
108
18.0%
54
9.0%
54
9.0%
54
9.0%
54
9.0%
54
9.0%
54
9.0%
54
9.0%
2
 
0.3%
Hiragana
ValueCountFrequency (%)
8
30.8%
4
15.4%
2
 
7.7%
2
 
7.7%
1
 
3.8%
1
 
3.8%
1
 
3.8%
1
 
3.8%
1
 
3.8%
1
 
3.8%
Other values (4)4
15.4%
CJK
ValueCountFrequency (%)
6
 
12.2%
4
 
8.2%
4
 
8.2%
4
 
8.2%
2
 
4.1%
1
 
2.0%
1
 
2.0%
1
 
2.0%
1
 
2.0%
1
 
2.0%
Other values (24)24
49.0%
ASCII
ValueCountFrequency (%)
E2
25.0%
T2
25.0%
C2
25.0%
21
12.5%
11
12.5%
None
ValueCountFrequency (%)
1
100.0%

mobile_career
Categorical

CONSTANT
MISSING
REJECTED

Distinct1
Distinct (%)50.0%
Missing3981
Missing (%)99.9%
Memory size31.2 KiB
i

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowi
2nd rowi

Common Values

ValueCountFrequency (%)
i2
 
0.1%
(Missing)3981
99.9%

Length

2022-09-12T22:17:13.053445image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-12T22:17:13.201960image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
i2
100.0%

Most occurring characters

ValueCountFrequency (%)
i2
100.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
i2
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII2
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i2
100.0%

mobile_model
Categorical

MISSING
UNIFORM

Distinct2
Distinct (%)100.0%
Missing3981
Missing (%)99.9%
Memory size31.2 KiB
P01E
P01F

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters8
Distinct characters5
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st rowP01E
2nd rowP01F

Common Values

ValueCountFrequency (%)
P01E1
 
< 0.1%
P01F1
 
< 0.1%
(Missing)3981
99.9%

Length

2022-09-12T22:17:13.322883image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-12T22:17:13.497775image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
p01e1
50.0%
p01f1
50.0%

Most occurring characters

ValueCountFrequency (%)
P2
25.0%
02
25.0%
12
25.0%
E1
12.5%
F1
12.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter4
50.0%
Decimal Number4
50.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
P2
50.0%
E1
25.0%
F1
25.0%
Decimal Number
ValueCountFrequency (%)
02
50.0%
12
50.0%

Most occurring scripts

ValueCountFrequency (%)
Latin4
50.0%
Common4
50.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
P2
50.0%
E1
25.0%
F1
25.0%
Common
ValueCountFrequency (%)
02
50.0%
12
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII8
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
P2
25.0%
02
25.0%
12
25.0%
E1
12.5%
F1
12.5%

cancellation_classification
Categorical

CONSTANT
MISSING
REJECTED

Distinct1
Distinct (%)1.9%
Missing3929
Missing (%)98.6%
Memory size31.2 KiB
ノーチャージキャンセル
54 

Length

Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

Total characters594
Distinct characters9
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowノーチャージキャンセル
2nd rowノーチャージキャンセル
3rd rowノーチャージキャンセル
4th rowノーチャージキャンセル
5th rowノーチャージキャンセル

Common Values

ValueCountFrequency (%)
ノーチャージキャンセル54
 
1.4%
(Missing)3929
98.6%

Length

2022-09-12T22:17:13.614354image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-12T22:17:13.736279image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
ノーチャージキャンセル54
100.0%

Most occurring characters

ValueCountFrequency (%)
108
18.2%
108
18.2%
54
9.1%
54
9.1%
54
9.1%
54
9.1%
54
9.1%
54
9.1%
54
9.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter486
81.8%
Modifier Letter108
 
18.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
108
22.2%
54
11.1%
54
11.1%
54
11.1%
54
11.1%
54
11.1%
54
11.1%
54
11.1%
Modifier Letter
ValueCountFrequency (%)
108
100.0%

Most occurring scripts

ValueCountFrequency (%)
Katakana486
81.8%
Common108
 
18.2%

Most frequent character per script

Katakana
ValueCountFrequency (%)
108
22.2%
54
11.1%
54
11.1%
54
11.1%
54
11.1%
54
11.1%
54
11.1%
54
11.1%
Common
ValueCountFrequency (%)
108
100.0%

Most occurring blocks

ValueCountFrequency (%)
Katakana594
100.0%

Most frequent character per block

Katakana
ValueCountFrequency (%)
108
18.2%
108
18.2%
54
9.1%
54
9.1%
54
9.1%
54
9.1%
54
9.1%
54
9.1%
54
9.1%

answer
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing3983
Missing (%)100.0%
Memory size31.2 KiB

payment_method
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size31.2 KiB
1
2667 
0
1307 
2
 
9

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3983
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
12667
67.0%
01307
32.8%
29
 
0.2%

Length

2022-09-12T22:17:13.843075image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-12T22:17:13.976993image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
12667
67.0%
01307
32.8%
29
 
0.2%

Most occurring characters

ValueCountFrequency (%)
12667
67.0%
01307
32.8%
29
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3983
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
12667
67.0%
01307
32.8%
29
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common3983
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
12667
67.0%
01307
32.8%
29
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII3983
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
12667
67.0%
01307
32.8%
29
 
0.2%

Interactions

2022-09-12T22:16:56.322033image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T22:16:29.616121image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T22:16:31.557822image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T22:16:33.792563image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T22:16:36.629781image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T22:16:40.141618image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T22:16:42.494638image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T22:16:45.558429image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T22:16:47.633591image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T22:16:49.663039image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T22:16:51.689758image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T22:16:54.215233image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T22:16:56.481933image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T22:16:29.787973image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T22:16:31.724392image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T22:16:34.010293image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T22:16:37.063514image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T22:16:40.365482image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T22:16:42.786459image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T22:16:45.712334image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T22:16:47.785499image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T22:16:49.807948image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T22:16:51.845664image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T22:16:54.369137image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T22:16:56.642039image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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2022-09-12T22:16:54.535090image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T22:16:56.897099image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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2022-09-12T22:16:43.445056image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T22:16:46.138817image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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2022-09-12T22:16:57.140144image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T22:16:30.432191image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T22:16:32.557413image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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2022-09-12T22:16:38.122863image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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2022-09-12T22:16:43.952742image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T22:16:46.397657image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T22:16:48.459479image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T22:16:50.489783image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T22:16:52.963465image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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2022-09-12T22:16:30.588438image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T22:16:32.713657image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T22:16:35.077252image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T22:16:38.340730image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T22:16:41.354873image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T22:16:44.220576image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T22:16:46.553561image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T22:16:48.616382image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T22:16:50.645686image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T22:16:53.130362image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T22:16:55.251214image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T22:16:57.401026image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T22:16:30.713433image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T22:16:32.869904image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T22:16:35.275442image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T22:16:38.552597image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T22:16:41.514773image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T22:16:44.421453image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T22:16:46.698472image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T22:16:48.766290image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T22:16:50.782602image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T22:16:53.273760image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T22:16:55.370691image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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2022-09-12T22:16:38.788452image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T22:16:41.671254image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T22:16:44.820732image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T22:16:46.862748image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T22:16:48.918198image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T22:16:50.926417image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T22:16:53.436661image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T22:16:55.539657image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T22:16:57.692863image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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2022-09-12T22:16:41.830187image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T22:16:44.978354image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T22:16:47.021032image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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2022-09-12T22:16:51.072330image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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2022-09-12T22:16:55.713550image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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2022-09-12T22:16:33.495239image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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2022-09-12T22:16:49.374717image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T22:16:51.356962image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T22:16:53.895430image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T22:16:56.013413image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T22:16:58.116086image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T22:16:31.432825image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T22:16:33.660879image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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2022-09-12T22:16:49.525122image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T22:16:51.535856image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T22:16:54.078318image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T22:16:56.174123image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2022-09-12T22:17:14.610853image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-09-12T22:17:14.985616image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-09-12T22:17:15.302424image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-09-12T22:17:15.625222image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-09-12T22:16:58.433089image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-09-12T22:17:00.244657image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-09-12T22:17:00.840533image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-09-12T22:17:01.229341image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

booking_statusrakuten_booking_numbercompany_booking_numberrakupack_booking_numberrequest_date_timerequest_confirmed_date_timecancel_request_date_timecancellation_day_timenamber_of_passengersnumber_of_childrenflight_numberaccommodation_nameaccommodation_addressaccommodation_phone_numberaccommodation_booking_numberpickup_methodpickup_date_timepickup_shop_codepickup_shop_namereturn_date_timereturn_shop_codereturn_shop_namecompany_car_class_codedetail_car_class_codedetail_car_class_nametransmissioncampaigncar_attributebasic_pricedrop_off_feeoptions_total_feenight_fee_pickupnight_fee_returnsubtotal_amountcouponpointtotal_amounttaxable_amountnon_taxable_amountcancel_feememooptionscancellation_reasonmobile_careermobile_modelcancellation_classificationanswerpayment_method
00RC32457434637117616NaNNaN2016/2/16 17:41NaN2016/2/16 17:47NaN30SKY771NaNNaNNaNNaN来店2016/2/21 14:00638新千歳空港店(ニコレンお客様大賞受賞店)2016/2/24 14:00638新千歳空港店(ニコレンお客様大賞受賞店)000AT★楽天限定★4WD・スタッドレスタイヤ標準装備でラクラクドライブ♪\r\n早い者勝ちの特別プラン◎楽しむなら、ニコニコレンタカーで!NaN80000324000112400001124000NaN[0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1]NaNNaNNaNNaNNaN0
11RC32457434645237633NaNNaN2016/2/16 17:552016/2/16 18:18NaNNaN30SKY771NaNNaNNaNNaN来店2016/2/21 14:00638新千歳空港店(ニコレンお客様大賞受賞店)2016/2/24 14:00638新千歳空港店(ニコレンお客様大賞受賞店)000AT★楽天限定★4WD・スタッドレスタイヤ標準装備でラクラクドライブ♪\r\n早い者勝ちの特別プラン◎楽しむなら、ニコニコレンタカーで!NaN80000324000112405000107401124000NaN[0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1]NaNNaNNaNNaNNaN1
21RC62457437572294437NaNNaN2016/2/19 15:532016/2/19 16:01NaNNaN10ANA4837NaNNaNNaNNaN来店2016/2/29 19:30638新千歳空港店(ニコレンお客様大賞受賞店)2016/3/2 13:00638新千歳空港店(ニコレンお客様大賞受賞店)011AT★楽天限定★4WD・スタッドレスタイヤ標準装備でラクラクドライブ♪\r\n早い者勝ちの特別プラン◎楽しむなら、ニコニコレンタカーで!NaN550002160007660007660766000NaN[0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1]NaNNaNNaNNaNNaN1
31RC72457438509836329NaNNaN2016/2/20 14:092016/2/20 15:43NaNNaN10BC171NaNNaNNaNNaN来店2016/3/12 10:00638新千歳空港店(ニコレンお客様大賞受賞店)2016/3/18 18:30638新千歳空港店(ニコレンお客様大賞受賞店)122AT★楽天限定★4WD・スタッドレスタイヤ標準装備でラクラクドライブ♪\r\n早い者勝ちの特別プラン◎楽しむなら、ニコニコレンタカーで!NaN1710007560002466000246602466000NaN[0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1]NaNNaNNaNNaNNaN1
41RC72457438821737647NaNNaN2016/2/20 22:492016/2/21 8:58NaNNaN20ANA4723NaNNaNNaNNaN来店2016/2/21 15:00638新千歳空港店(ニコレンお客様大賞受賞店)2016/2/23 15:00638新千歳空港店(ニコレンお客様大賞受賞店)233AT★楽天限定★4WD・スタッドレスタイヤ標準装備でラクラクドライブ♪\r\n早い者勝ちの特別プラン◎楽しむなら、ニコニコレンタカーで!NaN1300003240001624000162401624000NaN[0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1]NaNNaNNaNNaNNaN1
50RC12457439006207854NaNNaN2016/2/21 0:102016/2/21 10:412016/3/5 20:192016/3/5 20:4410ADO025アパホテル<札幌>北海道札幌市中央区南二条西七丁目10-1011-281-8111RYa05nwdf6来店2016/3/14 16:00638新千歳空港店(ニコレンお客様大賞受賞店)2016/3/15 15:00638新千歳空港店(ニコレンお客様大賞受賞店)044AT【宿泊予約者限定!!】1500店突破記念!早い者勝ち!ETC・ナビ付・ありがとうキャンペーン☆☆NaN290001080003980000398000チェックイン:2016/03/14 17:00\r\nチェックアウト:2016/03/15[0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1]NaNNaNNaNNaNNaN1
61RC22457440391040902NaNNaN2016/2/22 10:512016/2/22 10:54NaNNaN10JAL511ホテルリブマックス千歳北海道千歳市清水町3-14-10123-23-8100RYa05iwk9x来店2016/2/27 12:00638新千歳空港店(ニコレンお客様大賞受賞店)2016/2/29 12:00638新千歳空港店(ニコレンお客様大賞受賞店)044AT【宿泊予約者限定!!】1500店突破記念!早い者勝ち!ETC・ナビ付・ありがとうキャンペーン☆☆NaN540003240008640008640864000チェックイン:2016/02/27 23:00\r\nチェックアウト:2016/02/29[0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1]NaNNaNNaNNaNNaN0
71RC22457440589801668NaNNaN2016/2/22 16:232016/2/23 11:42NaNNaN72航空便利用なしNaNNaNNaNNaN来店2016/3/11 10:00638新千歳空港店(ニコレンお客様大賞受賞店)2016/3/15 17:00638新千歳空港店(ニコレンお客様大賞受賞店)355AT★楽天限定★4WD・スタッドレスタイヤ標準装備でラクラクドライブ♪\r\n早い者勝ちの特別プラン◎楽しむなら、ニコニコレンタカーで!NaN1600000001600010000150001600000NaN[0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0]NaNNaNNaNNaNNaN1
81RC22457440849212853NaNNaN2016/2/22 23:352016/2/23 11:47NaNNaN20ANA4715NaNNaNNaNNaN来店2016/3/23 11:00638新千歳空港店(ニコレンお客様大賞受賞店)2016/3/25 16:00638新千歳空港店(ニコレンお客様大賞受賞店)122AT★楽天限定★4WD・スタッドレスタイヤ標準装備でラクラクドライブ♪\r\n早い者勝ちの特別プラン◎楽しむなら、ニコニコレンタカーで!NaN75000000750001007400750000NaN[0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0]NaNNaNNaNNaNNaN1
91RC32457441405033534NaNNaN2016/2/23 11:152016/2/23 11:31NaNNaN10JAL511NaNNaNNaNNaN来店2016/2/27 11:00638新千歳空港店(ニコレンお客様大賞受賞店)2016/2/28 20:00638新千歳空港店(ニコレンお客様大賞受賞店)011AT★楽天限定★4WD・スタッドレスタイヤ標準装備でラクラクドライブ♪\r\n早い者勝ちの特別プラン◎楽しむなら、ニコニコレンタカーで!NaN550000005500005500550000NaN[0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]NaNNaNNaNNaNNaN1

Last rows

booking_statusrakuten_booking_numbercompany_booking_numberrakupack_booking_numberrequest_date_timerequest_confirmed_date_timecancel_request_date_timecancellation_day_timenamber_of_passengersnumber_of_childrenflight_numberaccommodation_nameaccommodation_addressaccommodation_phone_numberaccommodation_booking_numberpickup_methodpickup_date_timepickup_shop_codepickup_shop_namereturn_date_timereturn_shop_codereturn_shop_namecompany_car_class_codedetail_car_class_codedetail_car_class_nametransmissioncampaigncar_attributebasic_pricedrop_off_feeoptions_total_feenight_fee_pickupnight_fee_returnsubtotal_amountcouponpointtotal_amounttaxable_amountnon_taxable_amountcancel_feememooptionscancellation_reasonmobile_careermobile_modelcancellation_classificationanswerpayment_method
39730RC62458480075606385NaNNaN2018/12/28 2:06NaN2018/12/29 16:49NaN20NH775NaNNaNNaNNaN来店2019/3/8 14:00638新千歳空港店(ニコレンお客様大賞受賞店)2019/3/8 18:30638新千歳空港店(ニコレンお客様大賞受賞店)02222ATNaNNaN344001080004520000452000NaN[0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1]NaNNaNNaNNaNNaN1
39741RC62458480083666421NaNNaN2018/12/28 2:192019/1/4 11:30NaNNaN20ADO119NaNNaNNaNNaN来店2019/3/22 14:30638新千歳空港店(ニコレンお客様大賞受賞店)2019/3/24 9:30638新千歳空港店(ニコレンお客様大賞受賞店)044AT★楽天限定★4WD・スタッドレスタイヤ標準装備でラクラクドライブ♪\r\n早い者勝ちの特別プラン◎楽しむなら、ニコニコレンタカーで!NaN600002160008160100007160816000NaN[0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1]NaNNaNNaNNaNNaN0
39751RC62458480656227938NaNNaN2018/12/28 18:132018/12/28 19:45NaNNaN31NKQ6SNNaNNaNNaNNaN来店2018/12/29 10:00638新千歳空港店(ニコレンお客様大賞受賞店)2019/1/2 17:00638新千歳空港店(ニコレンお客様大賞受賞店)044AT★楽天限定★4WD・スタッドレスタイヤ標準装備でラクラクドライブ♪\r\n早い者勝ちの特別プラン◎楽しむなら、ニコニコレンタカーで!NaN1850005400002390000239002390000NaN[0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1]NaNNaNNaNNaNNaN1
39761RC72458481600650001NaNNaN2018/12/29 16:412018/12/29 16:50NaNNaN10GK101NaNNaNNaNNaN来店2018/12/30 8:30638新千歳空港店(ニコレンお客様大賞受賞店)2019/1/2 18:00638新千歳空港店(ニコレンお客様大賞受賞店)72525AT☆ハイブリッド限定プラン☆\r\n燃費のいいプリウス・アクアなどで快適ドライブ!\r\n★スタットレスタイヤ標準装備★\r\n台数限定プランです。お早目にご予約下さい!!NaN2250006480002173520000197352173500NaN[0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1]悪天候で欠航の為、次の日の出発に変更NaNNaNNaNNaN0
39771RC72458481763540535NaNNaN2018/12/29 21:122018/12/30 7:37NaNNaN40MM113NaNNaNNaNNaN来店2018/12/30 10:30638新千歳空港店(ニコレンお客様大賞受賞店)2018/12/31 17:30638新千歳空港店(ニコレンお客様大賞受賞店)000AT★楽天限定★4WD・スタッドレスタイヤ標準装備でラクラクドライブ♪\r\n早い者勝ちの特別プラン◎楽しむなら、ニコニコレンタカーで!NaN900002160001116000111601116000NaN[0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1]NaNNaNNaNNaNNaN1
39781RC12458482084861169NaNNaN2018/12/30 2:212018/12/30 8:27NaNNaN20SKY711NaNNaNNaNNaN来店2019/1/8 13:00638新千歳空港店(ニコレンお客様大賞受賞店)2019/1/11 9:00638新千歳空港店(ニコレンお客様大賞受賞店)044AT★楽天限定★4WD・スタッドレスタイヤ標準装備でラクラクドライブ♪\r\n早い者勝ちの特別プラン◎楽しむなら、ニコニコレンタカーで!NaN850003240001174015000102401174000NaN[0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1]NaNNaNNaNNaNNaN1
39791RC12458482732382602NaNNaN2018/12/30 20:202019/1/4 11:25NaNNaN30CI134NaNNaNNaNNaN来店2019/2/9 19:30638新千歳空港店(ニコレンお客様大賞受賞店)2019/2/17 13:00638新千歳空港店(ニコレンお客様大賞受賞店)044AT★楽天限定★4WD・スタッドレスタイヤ標準装備でラクラクドライブ♪\r\n早い者勝ちの特別プラン◎楽しむなら、ニコニコレンタカーで!NaN2355001296000365101500200348103651000NaN[0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1]NaNNaNNaNNaNNaN1
39801RC12458482752012662NaNNaN2018/12/30 20:532018/12/31 9:33NaNNaN10ana55NaNNaNNaNNaN来店2019/1/14 10:00638新千歳空港店(ニコレンお客様大賞受賞店)2019/1/15 17:00638新千歳空港店(ニコレンお客様大賞受賞店)355AT★楽天限定★4WD・スタッドレスタイヤ標準装備でラクラクドライブ♪\r\n早い者勝ちの特別プラン◎楽しむなら、ニコニコレンタカーで!NaN800000008000150006500800000NaN[0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0]NaNNaNNaNNaNNaN0
39811RC22458483289673225NaNNaN2018/12/31 8:022018/12/31 9:26NaNNaN10ANA987NaNNaNNaNNaN来店2019/1/8 8:00638新千歳空港店(ニコレンお客様大賞受賞店)2019/1/9 17:00638新千歳空港店(ニコレンお客様大賞受賞店)044AT★楽天限定★4WD・スタッドレスタイヤ標準装備でラクラクドライブ♪\r\n早い者勝ちの特別プラン◎楽しむなら、ニコニコレンタカーで!NaN42000000420070003500420000NaN[0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0]NaNNaNNaNNaNNaN1
39821RC22458483647914137NaNNaN2018/12/31 17:592019/1/1 8:23NaNNaN41DJ1NaNNaNNaNNaN来店2019/1/1 10:00638新千歳空港店(ニコレンお客様大賞受賞店)2019/1/2 17:00638新千歳空港店(ニコレンお客様大賞受賞店)72525AT☆ハイブリッド限定プラン☆\r\n燃費のいいプリウス・アクアなどで快適ドライブ!\r\n★スタットレスタイヤ標準装備★\r\n台数限定プランです。お早目にご予約下さい!!NaN1118002160001334000133401334000NaN[0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1]NaNNaNNaNNaNNaN0